hypothesis of social media addiction

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

A review of theories and models applied in studies of social media addiction and implications for future research

Affiliations.

1 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
2 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
PMID: 33268185
DOI: 10.1016/j.addbeh.2020.106699

With the increasing use of social media, the addictive use of this new technology also grows. Previous studies found that addictive social media use is associated with negative consequences such as reduced productivity, unhealthy social relationships, and reduced life-satisfaction. However, a holistic theoretical understanding of how social media addiction develops is still lacking, which impedes practical research that aims at designing educational and other intervention programs to prevent social media addiction. In this study, we reviewed 25 distinct theories/models that guided the research design of 55 empirical studies of social media addiction to identify theoretical perspectives and constructs that have been examined to explain the development of social media addiction. Limitations of the existing theoretical frameworks were identified, and future research areas are proposed.

Keywords: Facebook addiction; Internet addiction; Literature review; Problematic use; Social media addiction; Theoretical framework.

PubMed Disclaimer

Publication types

Search in MeSH

Related information

Linkout - more resources, full text sources.

ClinicalKey
Elsevier Science

Other Literature Sources

scite Smart Citations
Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

To read this content please select one of the options below:

Please note you do not have access to teaching notes, exploring the mechanism of social media addiction: an empirical study from wechat users.

Internet Research

ISSN : 1066-2243

Article publication date: 12 May 2020

Issue publication date: 22 June 2020

The problematic use of social media progressively worsens among a large proportion of users. However, the theory-driven investigation into social media addiction behavior remains far from adequate. Among the countable information system studies on the dark side of social media, the focus lies on users' subjective feelings and perceived value. The technical features of the social media platform have been ignored. Accordingly, this study explores the formation of social media addiction considering the perspectives of users and social media per se on the basis of extended motivational framework and attachment theory.

Design/methodology/approach

This study investigates the formation of social media addiction with particular focus on WeChat. A field survey with 505 subjects of WeChat users was conducted to investigate the research model.

Results demonstrate that social media addiction is determined by individuals' emotional and functional attachment to the platform. These attachments are in turn influenced by motivational (perceived enjoyment and social interaction) and technical (informational support, system quality and personalization) factors.

Originality/value

First, this study explains the underlying mechanism of how users develop social media addiction. Second, it highlights the importance of users' motivations and emotional dependence at this point. It also focuses on the technical system of the platform that plays a key role in the formation of addictive usage behavior. Third, it extends attachment theory to the context of social media addictive behavior.

Attachment theory
Socio-technical framework
Social media

Cao, X. , Gong, M. , Yu, L. and Dai, B. (2020), "Exploring the mechanism of social media addiction: an empirical study from WeChat users", Internet Research , Vol. 30 No. 4, pp. 1305-1328. https://doi.org/10.1108/INTR-08-2019-0347

Emerald Publishing Limited

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

Social Media Addiction

The Cause and Result of Growing Social Problems

Living reference work entry
First Online: 21 June 2023
Cite this living reference work entry

Troy Smith 7

113 Accesses

1 Citations

Accompanying the growth and increase in popularity of social media have been negative psychosocial and psychological effects associated with its excessive use. Research has shown a positive relationship between addiction-like behaviors associated with social media addiction (SMA) and psychological factors such as loneliness and low self-esteem, which demonstrate a congruency with recognized behavioral addictions. Adding to this congruency are the identified negative outcomes associated with SMA, which include difficulties in time perception, time management, maintaining interpersonal relationships, academic performance and increased prevalence of depression. According to the components model of addiction, the maladaptive behaviors/symptoms associated with problematic social media use (addiction) can be grouped into six dimensions, salience, tolerance, withdrawal, mood modification, conflict, and relapse. Studies have also identified several antecedents related to individual personality traits, fulfillment of psychological needs (relatedness, self-presentation, and social interaction), and perceived discrepancies between current and desired (or expected) interpersonal relationships (e.g., loneliness and low self-esteem). This chapter discusses the current understanding of SMA including its definition, measurement tools, and consequences. Further, it examines the underlying psychological and physiological explanations for addictive behaviors arising from social media use. The examination is based on a review of current theoretical understanding and the range of empirical studies, which examines the phenomena. Lastly, it highlights proposed social and policy approaches to alleviate the problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Agbaria, Q. (2022). Cognitive behavioral intervention in dealing with internet addiction among Arab teenagers in Israel. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-021-00733-6

Ahmed, E., & Vaghefi, I. (2021). Social media addiction: A systematic review through cognitive-behavior model of pathological use . http://hdl.handle.net/10125/71422

Aladwani, A. M., & Almarzouq, M. (2016). Understanding compulsive social media use: The premise of complementing self-conceptions mismatch with technology. Computers in Human Behavior, 60 , 575–581. https://doi.org/10.1016/j.chb.2016.02.098

Article Google Scholar

Andreassen, C. S. (2015). Online social network site addiction: A comprehensive review. Current Addiction Reports, 2 (2), 175–184. https://doi.org/10.1007/s40429-015-0056-9

Andreassen, C., Torsheim, T., Brunborg, G., & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110 (2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30 (2), 252–262. https://doi.org/10.1037/adb0000160

Andreassen, C. S., Pallesen, S., & Griffiths, M. D. (2017). The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors, 64 , 287–293. https://doi.org/10.1016/j.addbeh.2016.03.006

Atroszko, P., Balcerowska, J., Bereznowski, P., Biernatowska, A., Pallesen, S., & Andreassen, C. (2018). Facebook addiction among polish undergraduate students: Validity of measurement and relationship with personality and well-being. Computers in Human Behavior, 85 , 329–338. https://doi.org/10.1016/j.chb.2018.04.001

Atroszko, P. A., Atroszko, B., & Charzyńska, E. (2021). Subpopulations of addictive behaviors in different sample types and their relationships with gender, personality, and well-being: Latent profile vs. latent class analysis. International Journal of Environmental Research and Public Health, 18 (16), 8590. https://doi.org/10.3390/ijerph18168590

Azizi, S. M., Soroush, A., & Khatony, A. (2019). The relationship between social networking addiction and academic performance in Iranian students of medical sciences: A cross-sectional study. BMC Psychology, 7 (1), 28. https://doi.org/10.1186/s40359-019-0305-0

Balakrishnan, J., & Griffiths, M. D. (2017). SMA: What is the role of content in YouTube? Journal of Behavioral Addictions, 6 (3), 364–377. https://doi.org/10.1556/2006.6.2017.058

Balcerowska, J., Bereznowski, P., Biernatowska, A., Atroszko, P., Pallesen, S., & Andreassen, C. (2020). Is it meaningful to distinguish between Facebook addiction and SNS addiction? Psychometric analysis of Facebook addiction and SNS addiction scales. Current Psychology . https://doi.org/10.1007/s12144-020-00625-3

Baltaci, Ö. (2019). The predictive relationships between the SMA and social anxiety, loneliness, and happiness. International Journal of Progressive Education, 15 (4), 73–82. https://doi.org/10.29329/ijpe.2019.203.6

Bányai, F., Zsila, Á., Király, O., Maraz, A., Elekes, Z., Griffiths, M. D., Andreassen, C. S., & Demetrovics, Z. (2017). Problematic social media use: Results from a large-scale nationally representative adolescent sample. PLoS One, 12 (1), e0169839. https://doi.org/10.1371/journal.pone.0169839

Bata, H., Pentina, I., Tarafdar, M., & Pullins, E. B. (2018). Mobile social networking and salesperson maladaptive dependence behaviors. Computers in Human Behavior, 81 , 235–249. https://doi.org/10.1016/j.chb.2017.12.025

Benson, V., Hand, C., & Hartshorne, R. (2019). How compulsive use of social media affects performance: Insights from the UK by purpose of use. Behaviour & Information Technology, 38 (6), 549–563. https://doi.org/10.1080/0144929X.2018.1539518

Błachnio, A., Przepiorka, A., Senol-Durak, E., Durak, M., & Sherstyuk, L. (2017). The role of personality traits in Facebook and internet addictions: A study on polish, Turkish, and Ukrainian samples. Computers in Human Behavior, 68 , 269–275. https://doi.org/10.1016/j.chb.2016.11.037

Boer, M., Stevens, G. W. J. M., Finkenauer, C., de Looze, M. E., & van den Eijnden, R. J. J. M. (2021). Social media use intensity, social media use problems, and mental health among adolescents: Investigating directionality and mediating processes. Computers in Human Behavior, 116 , 106645. https://doi.org/10.1016/j.chb.2020.106645

Brailovskaia, J., Teismann, T., & Margraf, J. (2020). Positive mental health mediates the relationship between Facebook addiction disorder and suicide-related outcomes: A longitudinal approach. Cyberpsychology, Behavior and Social Networking, 23 (5), 346–350. https://doi.org/10.1089/cyber.2019.0563

Brevers, D., & Turel, O. (2019). Strategies for self-controlling social media use: Classification and role in preventing SMA symptoms. Journal of Behavioral Addictions, 8 (3), 554–563. https://doi.org/10.1556/2006.8.2019.49

Caci, B., Cardaci, M., Scrima, F., & Tabacchi, M. E. (2017). The dimensions of Facebook addiction as measured by Facebook addiction Italian questionnaire and their relationships with individual differences. Cyberpsychology, Behavior and Social Networking, 20 (4), 251–258. https://doi.org/10.1089/cyber.2016.0073

Cao, X., Gong, M., Yu, L., & Dai, B. (2020). Exploring the mechanism of social media addiction: An empirical study from WeChat users. Internet Research, 30 (4), 1305–1328. https://doi.org/10.1108/INTR-08-2019-0347

Caplan, S. E. (2005). A social skill account of problematic internet use. Journal of Communication, 55 (4), 721–736. https://doi.org/10.1111/j.1460-2466.2005.tb03019.x

Caplan, S. E. (2006). Relations among loneliness, social anxiety, and problematic internet use. Cyber Psychology & Behavior, 10 (2), 234–242.

Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic internet use. Cyberpsychology & Behavior, 10 (2), 234–242. https://doi.org/10.1089/cpb.2006.9963

Caplan, S. E. (2010). Theory and measurement of generalized problematic internet use: A two-step approach. Computers in Human Behavior, 26 (5), 1089–1097. https://doi.org/10.1016/j.chb.2010.03.012

Casale, S., & Fioravanti, G. (2018). Why narcissists are at risk for developing Facebook addiction: The need to be admired and the need to belong. Addictive Behaviors, 76 , 312–318. https://doi.org/10.1016/j.addbeh.2017.08.038

Chen, A. (2019). From attachment to addiction: The mediating role of need satisfaction on social networking sites. Computers in Human Behavior, 98 , 80–92. https://doi.org/10.1016/j.chb.2019.03.034

Cheng, C., Lau, Y., Chan, L., & Luk, J. W. (2021). Prevalence of SMA across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values. Addictive Behaviors, 117 , 106845. https://doi.org/10.1016/j.addbeh.2021.106845

Cheung, J. C.-S., Chan, K. H.-W., Lui, Y.-W., Tsui, M.-S., & Chan, C. (2018). Psychological well-being and adolescents’ internet addiction: A school-based cross-sectional study in Hong Kong. Child and Adolescent Social Work Journal, 35 (5), 477–487. https://doi.org/10.1007/s10560-018-0543-7

Choi, S., Chu, S.-C., & Kim, Y. (2012). Culture-laden social engagement: A comparative study of social relationships in social networking sites among American, Chinese and Korean users. In K. S. Amant & S. Kelsey (Eds.), Computer-mediated communication across cultures: International interactions in online environments . IGI Global. https://doi.org/10.4018/978-1-60960-833-0

Chapter Google Scholar

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience . Cambridge University Press.

Google Scholar

Dogan, V. (2019). Why do people experience the fear of missing out (FoMO)? Exposing the link between the self and the FoMO through self-construal. Journal of Cross-Cultural Psychology, 50 (4), 524–538. https://doi.org/10.1177/0022022119839145

Elphinston, R. A., & Noller, P. (2011). Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior and Social Networking, 14 (11), 631–635. https://doi.org/10.1089/cyber.2010.0318

Ercengiz, M. (2019). The effectiveness of ACT based psycho-education program on social media disorder. International Online Journal of Educational Sciences, 11 (1). https://doi.org/10.15345/iojes.2019.01.002

Foroughi, B., Iranmanesh, M., Nikbin, D., & Hyun, S. S. (2019). Are depression and social anxiety the missing link between Facebook addiction and life satisfaction? The interactive effect of needs and self-regulation. Telematics and Informatics, 43 , 101247. https://doi.org/10.1016/j.tele.2019.101247

Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10 (4), 191–197. https://doi.org/10.1080/14659890500114359

He, Q., Turel, O., & Bechara, A. (2017). Brain anatomy alterations associated with social networking site (SNS) addiction. Scientific Reports, 7 (1), 45064. https://doi.org/10.1038/srep45064

Ho, S. S., Lwin, M. O., & Lee, E. W. J. (2017). Till logout do us part? Comparison of factors predicting excessive social network sites use and addiction between Singaporean adolescents and adults. Computers in Human Behavior, 75 , 632–642. https://doi.org/10.1016/j.chb.2017.06.002

Holland, G., & Tiggemann, M. (2016). A systematic review of the impact of the use of SNS on body image and disordered eating outcomes. Body Image, 17 , 100–110. https://doi.org/10.1016/j.bodyim.2016.02.008

Hou, Y., Xiong, D., Jiang, T., Song, L., & Wang, Q. (2019). Social media addiction: Its impact, mediation, and intervention. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 13 (1). https://doi.org/10.5817/CP2019-1-4

Hunt, M. G., Marx, R., Lipson, C., & Young, J. (2018). No more FOMO: Limiting social media decreases loneliness and depression. Journal of Social and Clinical Psychology, 37 (10), 751–768. https://doi.org/10.1521/jscp.2018.37.10.751

Karahanna, E., Xu, S., Xu, Y., & Zhang, N. (Andy). (2018). The needs–affordances–features perspective for the use of social media. MIS Quarterly, 42 (3), 737–756. https://doi.org/10.25300/MISQ/2018/11492

Kardaras, N. (2016). Glow kids: How screen addiction is hijacking our kids – And how to break the trance (1st ed.). St. Martin’s Press.

Kircaburun, K., & Griffiths, M. D. (2018). Instagram addiction and the big five of personality: The mediating role of self-liking. Journal of Behavioral Addictions, 7 (1), 1–13. https://doi.org/10.1556/2006.7.2018.15

Kircaburun, K., Alhabash, S., Tosuntaş, Ş. B., & Griffiths, M. D. (2020). Uses and gratifications of problematic social media use among university students: A simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. International Journal of Mental Health and Addiction, 18 (3), 525–547. https://doi.org/10.1007/s11469-018-9940-6

Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish college students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior and Social Networking, 16 (4), 279–284. https://doi.org/10.1089/cyber.2012.0249

Kumar, A. (2018). The effect of the COVID-19 pandemic on how social media use influences eating disorders and exercise motivation. UNF Graduate Theses and Dissertations . 1144. https://digitalcommons.unf.edu/etd/1144

Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction—A review of the psychological literature. International Journal of Environmental Research and Public Health, 8 (9), 3528–3552. https://doi.org/10.3390/ijerph8093528

Kwak, K. T., Choi, S. K., & Lee, B. G. (2014). SNS flow, SNS self-disclosure and post hoc interpersonal relations change: Focused on Korean Facebook user. Computers in Human Behavior, 31 , 294–304. https://doi.org/10.1016/j.chb.2013.10.046

Lee, Z. W. Y., Cheung, C. M. K., & Thadani, D. R. (2012). An investigation into the problematic use of Facebook. In 2012 45th Hawaii international conference on system sciences (pp. 1768–1776). https://doi.org/10.1109/HICSS.2012.106

Lu, X., Lu, Z., & Liu, C. (2020). Exploring TikTok use and non-use practices and experiences in China. In G. Meiselwitz (Ed.), Social computing and social media. Participation, user experience, consumer experience, and applications of social computing (Vol. 12195, pp. 57–70). Springer International Publishing. https://doi.org/10.1007/978-3-030-49576-3_5

Luo, T., Qin, L., Cheng, L., Wang, S., Zhu, Z., Xu, J., Chen, H., Liu, Q., Hu, M., Tong, J., Hao, W., Wei, B., & Liao, Y. (2021). Determination the cut-off point for the Bergen social media addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder. Journal of Behavioral Addictions . https://doi.org/10.1556/2006.2021.00025

Marengo, D., Montag, C., Sindermann, C., Elhai, J. D., & Settanni, M. (2021). Examining the links between active Facebook use, received likes, self-esteem and happiness: A study using objective social media data. Telematics and Informatics, 58 , 101523. https://doi.org/10.1016/j.tele.2020.101523

Marino, C., Gini, G., Vieno, A., & Spada, M. M. (2018). The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: A systematic review and meta-analysis. Journal of Affective Disorders, 226 , 274–281. https://doi.org/10.1016/j.jad.2017.10.007

Montag, C., Yang, H., & Elhai, J. D. (2021). On the psychology of TikTok use: A first glimpse from empirical findings. Frontiers in Public Health, 9 . https://doi.org/10.3389/fpubh.2021.641673

Müller, K. W., Dreier, M., Beutel, M. E., Duven, E., Giralt, S., & Wölfling, K. (2016). A hidden type of internet addiction? Intense and addictive use of SNS in adolescents. Computers in Human Behavior, 55 , 172–177. https://doi.org/10.1016/j.chb.2015.09.007

Omar, B., & Dequan, W. (2020). Watch, share or create: The influence of personality traits and user motivation on TikTok Mobile video usage. International Journal of Interactive Mobile Technologies, 14 (4), 121-137.

Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior and Social Networking, 17 (10), 652–657. https://doi.org/10.1089/cyber.2014.0070

Pellegrino, A., Stasi, A., & Bhatiasevi, V. (2022). Research trends in SMA and problematic social media use: A bibliometric analysis. Frontiers in Psychiatry, 13 , 1017506. https://doi.org/10.3389/fpsyt.2022.1017506

Peris, M., de la Barrera, U., Schoeps, K., & Montoya-Castilla, I. (2020). Psychological risk factors that predict social networking and internet addiction in adolescents. International Journal of Environmental Research and Public Health, 17 (12), 4598. https://doi.org/10.3390/ijerph17124598

Primack, B., Shensa, A., Sidani, J., Whaite, E., Lin, L., Rosen, D., Colditz, J., Radovic, A., & Miller, E. (2017). Social media use and perceived social isolation among Young adults in the U.S. American Journal of Preventive Medicine, 53 (1), 1–8. https://doi.org/10.1016/j.amepre.2017.01.010

Quittkat, H. L., Hartmann, A. S., Düsing, R., Buhlmann, U., & Vocks, S. (2019). Body dissatisfaction, importance of appearance, and body appreciation in men and women over the lifespan. Frontiers in Psychiatry, 10 , 864. https://doi.org/10.3389/fpsyt.2019.00864

Ricci, J. (2018). The growing case for SMA . The California State University. https://www.calstate.edu/csu-system/news/Pages/Social-Media-Addiction.aspx

Rrustemi, V., Hasani, E., Jusufi, G., & Mladenović, D. (2021). Social media in use: A uses and gratifications approach. Management, 26 (1), 201–217. https://doi.org/10.30924/mjcmi.26.1.12

Schinka, K. C., VanDulmen, M. H. M., Bossarte, R., & Swahn, M. (2012). Association between loneliness and suicidality during middle childhood and adolescence: Longitudinal effects and the role of demographic characteristics. The Journal of Psychology, 146 (1–2), 105–118. https://doi.org/10.1080/00223980.2011.584084

Sedgwick, R., Epstein, S., Dutta, R., & Ougrin, D. (2019). Social media, internet use and suicide attempts in adolescents. Current Opinion in Psychiatry, 32 (6), 534–541. https://doi.org/10.1097/YCO.0000000000000547

Seo, D., & Ray, S. (2019). Habit and addiction in the use of SNS: Their nature, antecedents, and consequences. Computers in Human Behavior, 99 , 109–125. https://doi.org/10.1016/j.chb.2019.05.018

Shahnawaz, M. G., & Rehman, U. (2020). Social networking addiction scale. Cogent Psychology, 7 (1), 1832032. https://doi.org/10.1080/23311908.2020.1832032

Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., & Dapretto, M. (2016). The power of the like in adolescence: Effects of peer influence on neural and behavioral responses to social media. Psychological Science, 27 (7), 1027–1035. https://doi.org/10.1177/0956797616645673

Skues, J. L., Williams, B., & Wise, L. (2012). The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students. CHB Computers in Human Behavior, 28 (6), 2414–2419.

Smith, T. (2022). Facebook addiction user risk profiles among college students: Identification of subclasses of addictive behaviors characterized by demographics and covariates using latent profile analysis. Telematics and Informatics Reports, 8 , 100027. https://doi.org/10.1016/j.teler.2022.100027

Smith, T. (2023). An exploratory investigation into social media use in Trinidad and Tobago: A comparison of Facebook and TikTok. Caribbean Journal of Multidisciplinary Studies, 1 (2), 88-112.

Smith, T., & Short, A. (2022). Needs affordance as a key factor in likelihood of problematic social media use: Validation, latent profile analysis and comparison of TikTok and Facebook problematic use measures. Addictive Behaviors, 129 , 107259. https://doi.org/10.1016/j.addbeh.2022.107259

Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28 (1), 78–106. https://doi.org/10.1016/j.dr.2007.08.002

Sun, Y., & Zhang, Y. (2021). A review of theories and models applied in studies of SMA and implications for future research. Addictive Behaviors, 114 , 106699. https://doi.org/10.1016/j.addbeh.2020.106699

Szcześniak, M., Bielecka, G., Madej, D., Pieńkowska, E., & Rodzeń, W. (2020). The role of self-esteem in the relationship between loneliness and life satisfaction in late adulthood: Evidence from Poland. Psychology Research and Behavior Management, 13 , 1201–1212. https://doi.org/10.2147/PRBM.S275902

Tan, W.-K., & Yang, C.-Y. (2014). Internet applications use and personality. Telematics and Informatics, 31 (1), 27–38. https://doi.org/10.1016/j.tele.2013.02.006

Tandon, A., Kaur, P., Dhir, A., & Mäntymäki, M. (2020). Sleepless due to social media? Investigating problematic sleep due to social media and social media sleep hygiene. Computers in Human Behavior, 113 , 106487. https://doi.org/10.1016/j.chb.2020.106487

Tolentino, J. C., & Schmidt, S. L. (2018). DSM-5 criteria and depression severity: Implications for clinical practice. Frontiers in Psychiatry, 9 , 450. https://doi.org/10.3389/fpsyt.2018.00450

Twenge, J. M. (2020). Increases in depression, self-harm, and suicide among U.S. adolescents after 2012 and links to technology use: Possible mechanisms. Psychiatric research and clinical Practice, 2 (1), 19–25. https://doi.org/10.1176/appi.prcp.20190015

van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016). The social media disorder scale. Computers in Human Behavior, 61 , 478–487. https://doi.org/10.1016/j.chb.2016.03.038

Walburg, V., Mialhes, A., & Moncla, D. (2016). Does school-related burnout influence problematic Facebook use? Children and Youth Services Review, 61 , 327–331. https://doi.org/10.1016/j.childyouth.2016.01.009

Wang, C., Lee, M. K. O., & Hua, Z. (2015). A theory of social media dependence: Evidence from microblog users. Decision Support Systems, 69 , 40–49. https://doi.org/10.1016/j.dss.2014.11.002

Wang, Q., Yang, M., & Zhang, W. (2021). Accessing the influence of perceived value on social attachment: Developing country perspective. Frontiers in Psychology, 12 , 760774. https://doi.org/10.3389/fpsyg.2021.760774

Wilksch, S. M., O’Shea, A., Ho, P., Byrne, S., & Wade, T. D. (2020). The relationship between social media use and disordered eating in young adolescents. International Journal of Eating Disorders, 53 (1), 96–106. https://doi.org/10.1002/eat.23198

Xanidis, N., & Brignell, C. M. (2016). The association between the use of social network sites, sleep quality and cognitive function during the day. Computers in Human Behavior, 55 , 121–126. https://doi.org/10.1016/j.chb.2015.09.004

Yang, S., Liu, Y., & Wei, J. (2016). Social capital on mobile SNS addiction: A perspective from online and offline channel integrations. Internet Research, 26 (4), 982–1000. https://doi.org/10.1108/IntR-01-2015-0010

Youssef, L., Hallit, R., Kheir, N., Obeid, S., & Hallit, S. (2020). Social media use disorder and loneliness: Any association between the two? Results of a cross-sectional study among Lebanese adults. BMC Psychology, 8 (1), 56. https://doi.org/10.1186/s40359-020-00421-5

Zhao, N., & Zhou, G. (2021). COVID-19 stress and addictive social media use (SMU): Mediating role of active use and social media flow. Frontiers in Psychiatry, 12 , 635546. https://doi.org/10.3389/fpsyt.2021.635546

Download references

Author information

Authors and affiliations.

Targeted Evidence-Based Research Solutions, Arouca, Trinidad and Tobago

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Troy Smith .

Rights and permissions

Reprints and permissions

Copyright information

About this entry

Cite this entry.

Smith, T. (2023). Social Media Addiction. In: The Palgrave Handbook of Global Social Problems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-68127-2_365-1

Download citation

DOI : https://doi.org/10.1007/978-3-030-68127-2_365-1

Received : 17 March 2023

Accepted : 22 March 2023

Published : 21 June 2023

Publisher Name : Palgrave Macmillan, Cham

Print ISBN : 978-3-030-68127-2

Online ISBN : 978-3-030-68127-2

eBook Packages : Springer Reference Social Sciences Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Find a journal
Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 04 October 2023

Predictors of social networking service addiction

Hyeon Jo ORCID: orcid.org/0000-0001-7442-4736 1 &
Eun-Mi Baek ORCID: orcid.org/0000-0002-0940-5819 2

Scientific Reports volume 13 , Article number: 16705 ( 2023 ) Cite this article

2408 Accesses

1 Altmetric

Metrics details

Mathematics and computing

The surge in social network services (SNS) usage has ignited concerns about potential addictive behaviors stemming from excessive engagement. This research focuses on pinpointing the primary determinants of SNS addiction by introducing a theoretical framework centered on flow, perceived enjoyment, and habit. A sample of 282 SNS users from South Korea was surveyed, and the gathered data was assessed through partial least squares structural equation modeling (PLS-SEM). The evaluation revealed that positive affect closely relates to flow and perceived enjoyment, whereas negative affect amplifies flow but diminishes perceived enjoyment. Additionally, the research underscored that social influence significantly shapes habits and affects perceived enjoyment. Notably, flow demonstrated a strong connection to addiction, and perceived enjoyment influenced both flow and habit significantly. Habit was directly linked to addiction. These insights pave the way for more in-depth studies on SNS addiction patterns and offer a foundation for devising effective strategies to mitigate its adverse effects.

The association between passive social network usage and depression/negative emotions with envy as a mediator

A taxonomy of technology design features that promote potentially addictive online behaviours

Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective

Introduction.

With the rapid proliferation of social network services (SNS), users' daily routines have evolved significantly 1 , 2 , 3 . SNS provides insights into acquaintances' updates, new product launches, and current events 4 , 5 , 6 . Given SNS's profound influence, users are dedicating more time to these platforms 7 . Among various tools, SNS apps are most frequented on smart devices 8 . Some users develop a habitual pattern of SNS usage, which, in extreme cases, turns addictive 9 , 10 . SNS addiction can be defined as an excessive, compulsive use of social media platforms that interferes with daily life, leading to negative consequences in physical, social, and mental well-being 11 . It involves an increased craving to engage on these platforms, leading to the neglect of offline relationships and daily responsibilities 12 . Ironically, this addiction can overshadow the genuine connections that SNSs aim to cultivate. Numerous studies have explored the variables driving SNS user behavior 13 , 14 , attributing psychological elements, social influencers, and predisposition to SNS addiction 15 , 16 , 17 . However, there remains a gap in comprehensively understanding social and psychological influences on addiction. This research seeks to bridge that gap by examining user affect, social stimuli, and mental states.

The rise in SNS over-reliance or addiction is a modern behavioral addiction that alarms researchers and mental health experts alike 11 . This trend is notably prevalent among university students, a major user group. Analyzing SNS addiction determinants among this group is crucial for multiple reasons. Firstly, high SNS engagement in students correlates with adverse psychological outcomes like depression, anxiety, and loneliness 18 , 19 . It also interferes with sleep and hampers academic success 20 . Thus, comprehending SNS addiction's roots can help alleviate these issues. Furthermore, university students are in a crucial life phase, establishing habits that might extend into their later life 21 . Recognizing and addressing addictive patterns during this period can circumvent future repercussions 22 . As a dominant SNS user group, understanding students' addiction can enable tailored intervention strategies. Hence, exploring SNS addiction's drivers among university students can advance mental health, foster efficient interventions, and deepen our grasp on behavioral addiction.

The dual factor model of Facebook use 23 posits that individuals use SNS to manage both positive and negative affects. Positive affect boosts user satisfaction and flow in SNS 24 . Users in a good mood can lose track of time on SNS, further enhancing their addictive tendencies. Negative affect, denoting distressing emotions 25 , also impacts SNS flow 24 . Those with high negative affect might use SNS reflexively to counter negative feelings. This behavior aligns with the manifestation of habits 26 . Higher negative affect can diminish enjoyment levels. Users might engage with these platforms to amplify positive feelings or mitigate negative ones. If such strategies become compulsive, they might foster SNS addiction. Moreover, individuals with pronounced negative affect might be susceptible to behavioral addictions like SNS addiction 27 , using them as coping mechanisms. While positive affect usually brings beneficial outcomes, in some contexts, it can contribute to SNS addiction. If SNS consistently evokes positive feelings, it might reinforce and lead to an addiction cycle. Some research highlights a positive correlation between positive affect and addictive behaviors 28 . Considering both affects allows a thorough study of the emotional aspects of SNS addiction among university students. These insights can guide the creation of interventions targeting addictive SNS behaviors.

Social influence represents the degree to which a person's attitudes, beliefs, and behaviors are affected by others 29 . Users who are highly influenced by acquaintances will use SNS more frequently. Users who use SNS more tend to become addicted to SNS 30 . Thus, users with a higher level of social influence may be more prone to SNS addiction. Users who hear a lot about SNS from their acquaintances may be more immersed in using SNS than those who do not. They may also habitually use SNS to check the influence of their surroundings. Because SNS essentially forms fun and motivation to use via social relationships, social influence may increase perceived enjoyment. Additionally, this study investigates the impacts of negative affect on addiction, flow, habit, and perceived enjoyment.

Flow, a concept introduced by Csikszentmihalyi 31 , describes the state where one becomes deeply engrossed in an activity, losing all sense of time and self-awareness. Such flow significantly influences online users' addiction 32 , 33 . Regarding SNS, flow is viewed as an addiction precursor 34 . This immersion makes users neglect other priorities, encouraging addictive behaviors. Perceived enjoyment is another driver. As per self-determination theory 35 , inherent satisfaction from activities motivates behavior. Thus, students enjoying SNS may overuse, leading to addiction 33 , 36 . Perceived enjoyment also impacts flow 37 , 38 . Habit, marked by automatic responses and lack of intent 39 , also drives SNS addiction, especially among university students. Habitual SNS usage can be an automatic reflex to triggers like boredom, potentially escalating to addiction 40 . Habit strength, denoted by SNS usage frequency, is a predictor of addiction 10 , 30 , 41 . By studying flow, enjoyment, and habit, we obtain a holistic view of the psychological dynamics underlying SNS addiction.

The primary objective of this study is to comprehensively examine the multifaceted relationship between individual emotional responses (both positive and negative affects), social influences, flow experiences, perceived enjoyment, habitual behaviors, and the potential development of addiction towards SNS among university students. Through this exploration, the research aims to shed light on the underlying psychological and behavioral dynamics that may predispose individuals to SNS addiction, thereby offering insights into potential intervention and prevention strategies tailored to this demographic.

Although there is an extensive body of literature addressing the determinants of addiction to SNS, certain theoretical gaps remain unbridged. Firstly, the bulk of the research tends to position positive and negative affect on a singular continuum, rather than recognizing them as distinct constructs. This oversimplification potentially obscures the individual contributions of each affective state to SNS addiction. Secondly, the intricate relationships between positive and negative affect and other psychological determinants, including flow, perceived enjoyment, and habit, remain underexplored, particularly within the demographic of university students. This paper addresses the above gaps in the literature and contributes to the field in following ways. Firstly, it takes a novel approach by considering positive affect and negative affect as independent variables, allowing for a more nuanced understanding of their respective roles in SNS addiction. Secondly, it extends the current literature by exploring the interplay between positive affect, negative affect, social influence, flow, perceived enjoyment, and habit in the context of SNS addiction among university students. The conceptual foundation of our study is rooted in the dual factor model of SNS Use, which emphasizes the regulatory function of both positive and negative affects in SNS engagement 23 . Anchored by Deci and Ryan’s self-determination theory 35 , we propose that inherent satisfaction derived from activities becomes a potent motivator of behavior, especially in the context of SNS usage. Csikszentmihalyi’s theory of Flow also informs our study, positing that users become deeply engrossed in activities, losing awareness of time, which, in the context of SNS, can precipitate addiction. Collectively, this theoretical framework will guide our exploration of the psychological and behavioral nuances influencing SNS addiction among university students.

This paper delves into such an unresearched dimension, shedding light on the intricate interplay of flow, habits, and perceived enjoyment as drivers of SNS addiction. While extant literature has ventured into the domains of health, loneliness, and attachment in relation to SNS addiction, the unique combination of factors examined herein offers a fresh perspective. This underscores the originality of our research, marking a distinct departure from conventional narratives. By merging previously disjointed variables and unveiling their collective impact on SNS addiction, we not only bridge a significant gap in the current scholarship but also provide readers with a compelling rationale to delve deeper into our findings. In so doing, we aspire to catalyze further academic discourse and innovative research in this domain.

This article is structured as follows. Section “ Literature review ” describes the theoretical background. Section “ Research model ” delineates the research model and hypotheses. Section “ Methodology ” covers data and measurement tools. Section “ Results ” presents the analysis results of the measurement model and structural model. Section “ Discussion ” shows the discussion. Finally, Section “ Conclusion ” introduces a summary, implications, and limitations.

Literature review

Over the past few decades, there has been a meteoric rise in the popularity and reach of SNS. This expansion has not only changed the way individuals communicate and interact but has also paved the way for a vast digital market. The ubiquitous nature of these platforms, combined with their design geared towards continuous engagement, has led to growing concerns among researchers, psychologists, and sociologists. The core of these concerns revolves around the potential addictive nature of these platforms. Given the profound impact of SNS on modern life, an increasing body of research has been dedicated to exploring the phenomenon of SNS addiction, its underlying causes, and its multifaceted implications on individual and societal well-being.

Affect, broadly categorized into positive and negative emotions, plays a pivotal role in determining how individuals interact with, perceive, and are influenced by SNS platforms. SNS addiction, characterized by excessive and compulsive use of SNS platforms despite negative repercussions 42 , has been closely tied to affective states. Positive affect often drives the 'reward-seeking' behavior, causing users to chase the dopamine rush associated with likes, comments, and social validation on these platforms 41 . Conversely, negative affect often results in escapism, where users resort to SNS to avoid or numb their negative feelings, eventually leading to addictive patterns 43 . Positive emotions have been linked to increased engagement with SNS platforms. Users in a positive mood state tend to share more, interact positively with others, and spend more time on SNS 44 , 45 , 46 . Furthermore, positive affect enhances the intrinsic motivation to use SNS, increasing the frequency and duration of usage 47 . There is evidence to suggest that people with high levels of positive affect use SNS as a medium to maintain and strengthen social connections, amplifying their feelings of social belonging and self-worth 48 . Contrarily, negative affect has a more nuanced relationship with SNS. While some research indicates that individuals experiencing negative emotions resort to SNS as a coping mechanism 49 , others argue that prolonged SNS use, especially passive browsing, can exacerbate negative emotions 50 . Moreover, the “social comparison theory” postulates that individuals with high levels of negative affect are more prone to compare themselves to others on SNS, which can amplify feelings of inadequacy and further deepen the negative emotional state 51 . In addition to affect, several studies have introduced attachments to explain SNS addiction. Monacis et al. 52 assessed the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale using confirmatory factor analysis. They evaluated five dimensions of adult attachment and clarified a theoretical relationship between SNS addiction and attachment style. Park and Oh 53 identified the key factors influencing SNS addiction in pre-service teachers, finding that anxiety attachment and avoidant attachment significantly elicit SNS addiction. Furthermore, insecure adult attachment was found to mediate the effect of covert narcissism on SNS addiction.

Social influence, broadly defined, encompasses the array of ways in which individuals change their thoughts, feelings, and behaviors as a result of interacting with others 54 . Several studies have linked the role of social influence to increasing time spent on SNS platforms. Algorithms designed to show content from close connections or popular trends create a reinforcing loop, wherein users continuously engage to stay updated and relevant 55 . Furthermore, the witnessing of peers frequently engaging with or endorsing certain content or platforms can create a normative behavior pattern, leading individuals to subconsciously conform and potentially enter into a cycle of addictive behavior 56 . While social influence plays a significant role, the extent of its impact on an individual can vary based on personal factors. Those with low self-esteem or a high need for social validation may be more susceptible to SNS addiction under strong social influence 51 . Conversely, individuals with strong personal resilience and critical media literacy might navigate SNS spaces without succumbing to addictive behaviors, despite prevalent social influences. In summary, the intricate relationship between societal impact and dependency, particularly within the domain of SNSs, presents a diverse and intricate area of exploration. As society becomes increasingly digital, comprehending these intricacies becomes crucial in fostering constructive online conduct and lessening the vulnerabilities of dependency.

Scholars have addressed the concepts of flow, perceived enjoyment, and habit to explain behaviors associated with addiction to SNSs. Flow has been observed in various digital experiences, including gaming, website browsing, and SNS usage 57 . This deep immersion can amplify the appeal of SNS, making users more prone to spend extended periods in such platforms. Researchers like Faiola et al. 58 have argued that achieving a state of flow can lead to repeated usage, which, over time, can contribute to addictive behaviors. This is because the gratification derived from such immersive experiences makes users more likely to seek them out repetitively. In the context of SNS, perceived enjoyment signifies the pleasure users derive from platform activities, be it scrolling, posting, or interacting with peers. Studies have identified perceived enjoyment as a strong predictor of SNS use 13 , 36 . Platforms that offer high levels of enjoyment can foster continued and increased usage. Over time, the search for this intrinsic pleasure can drive individuals to use SNS excessively. This compulsive need to seek out enjoyment can pave the way for addictive behavior patterns 11 . Furthermore, the daily ritual of checking and engaging with SNS can lead to the formation of strong habits 18 . With notifications and constant updates, SNS platforms are designed to encourage routine interactions, facilitating the transformation of casual usage into habitual behavior 59 , 60 . As habits become deeply ingrained, they can become automatic responses, often executed without much thought or conscious intention. This automaticity is concerning as users might find themselves compulsively checking SNS without any particular reason or prompt, signaling potential addiction 41 . Lee et al. 24 posited that SNS addiction has a significant impact on flow and user satisfaction, finding that addiction positively affects flow. Gong et al. 61 explored the factors affecting mobile SNS addiction, discovering that flow plays a crucial role in increasing the level of addiction. They also found that enjoyment, sociability, and informational value are major antecedents of flow, leading to a higher level of addiction. Seo and Ray 62 examined the effects of habit and addiction in SNS use, revealing that immersion and concern for social acceptance are significant factors in increasing addictive use. Habitual use was also shown to have a positive influence on addictive use. Focused immersion is conceptually similar to flow in this study.

Additionally, researchers have studied SNS addiction by various perspectives. Yang et al. 10 considered SNS addiction as pathological behavior in the context of mobile SNS, while high engagement was classified as non-pathological behavior. It was revealed that SNS enjoyment significantly impacts both addiction and high engagement, and that habit is significantly related to addiction. Osatuyi and Turel 30 examined the precursors of SNS addiction symptoms using the dual system theory. They found that habit affects SNS addiction symptoms both directly and indirectly. Social self-regulation was also significantly associated with SNS addiction symptoms. Pontes et al. 63 studied the role of cognitive-related factors and psychiatric distress in SNS addiction, demonstrating that fear of missing out, maladaptive cognitions, and psychiatric distress significantly contribute to addiction. Turel and Serenko 36 summarized three theoretical perspectives: the cognitive-behavioral model, the social skill model, and the socio-cognitive model. Griffiths 64 pointed out potential controversy over SNS addiction, suggesting improvements in methodological design, sample representativeness, and scale validity to bridge the gap between empirical findings.

Even with an abundance of studies focusing on these areas, a comprehensive exploration that seamlessly combines affect, social influence, flow, perceived enjoyment, and habit is noticeably absent. In light of this, our study endeavors to weave these constructs together, forging a cohesive blueprint that underscores their collective influence on SNS addiction. Through this endeavor, we aspire to deliver crucial insights to scholars and professionals alike, charting a path towards strategies that encourage healthier interactions with SNS platforms.

Research model

Figure 1 presents the research model for understanding the determinants of SNS addiction. This study elucidates the roles of flow, perceived enjoyment, and habit in leading to addiction. It proposes that positive affect, negative affect, and social influence have significant impacts on the precursors of addiction.

Research model.

Positive affect

Positive affect, as defined by Watson et al. 25 , pertains to the extent to which individuals feel active, alert, and enthusiastic. Previous research suggests a significant relationship between positive affect and flow 65 , 66 as well as perceived enjoyment 13 . In the context of SNS usage among college students, those with higher levels of positive affect may become more immersed in SNS activities to sustain these positive emotions, consequently exhibiting addictive behavior. They may experience a sense of joy due to their positive feelings and thus repetitively open the SNS app. Based on these observations, the current study posits that positive affect serves as a potent factor in the development of addiction, flow, habit, and perceived enjoyment.

Positive affect significantly influences flow.

Positive affect significantly influences perceived enjoyment.

Positive affect significantly influences habit.

Negative affect

Negative affect is characterized as a state of distress and unenjoyable engagement that encompasses a range of aversive mood states 25 . In the context of SNS use among college students, negative affect can significantly influence their engagement with these platforms. As students experience higher levels of negative affect, they may alter their flow and habits on SNS platforms in an attempt to mitigate these uncomfortable feelings. For instance, students might increase their usage of SNS as a coping mechanism to distract themselves from their negative emotions 67 , 68 . Alternatively, they could also withdraw from SNS platforms due to their decreased enjoyment derived from the platforms when in a negative mood state. Consequently, negative affect can significantly influence the flow, habit formation, and perceived enjoyment associated with SNS use.

Negative affect significantly influences flow.

Negative affect significantly influences perceived enjoyment.

Negative affect significantly influences habit.

Social influence

Social influence refers to the degree to which they are influenced by each other's actions in social relationships 69 . It includes relational norms and the identity that users feel like a member of society. Social influence significantly determines the intention to use SNS 70 , 71 . The ultimate purpose of users using SNS is to form and experience social relationships. In this context, users who are greatly influenced by their acquaintances may experience addiction and flow. The more people around users have an influence on SNS, the more users will try to use it repeatedly and enjoy it. Hence, social influence is believed to significantly affect addiction, flow, habit, and perceived enjoyment.

Social influence significantly influences flow.

Social influence significantly influences perceived enjoyment.

Social influence significantly influences habit.

Flow refers to the holistic experience that individuals feel when they act with total involvement 72 . It significantly affects addiction in several online contexts 32 , 33 . Focused immersion is positively related to the addictive use of SNS 62 . Flow has a significant association with SNS addiction 61 , 73 . Thus, one can expect that flow serves as a crucial factor in shaping addiction.

Flow significantly influences addiction.

Perceived enjoyment

Perceived enjoyment is a key intrinsic motivation for information system usage 74 . It plays a preeminent role in enhancing addiction to online behavior 33 . Perceived pleasure was also found to be significantly related to social networking addiction 75 , 76 , 77 . The more SNS users enjoy social media activities, the more they would be immersed and immersed in them. SNS enjoyment serves as the salient factor in generating addiction and habit 10 . Given the above, this study is expected to show that perceived enjoyment significantly impacts addiction, flow, and habit.

Perceived enjoyment significantly influences addiction.

Perceived enjoyment significantly influences flow.

Perceived enjoyment significantly influences habit.

Habit represents repeated patterns of behavior that occur automatically without conscious awareness 78 . Habit significantly drives the experience of addiction symptoms 30 and positively influences mobile SNS addiction 10 . Habitual use of SNS drives addictive use 62 . Hence, it is predicted that habit has a significant effect on addiction.

H6. Habit significantly influences addiction.

Methodology

This study was approved by the Institutional Review Board (IRB) of HJ Institute of Technology and Management (HJITM), ethical committee of HJITM (HJITM-IRB-22-10-0008). In accordance with the ethical guidelines provided by the committee, we ensured to obtain in written form informed consent from all the study participants. All participants were informed about the purpose and the nature of the study, their rights to anonymity and confidentiality, and their freedom to withdraw from the study at any time without penalty.

Subjects and data collection

This research focused on a specific demographic group, namely, full-time undergraduate students from various higher education institutions, due to their noted high engagement with SNS and the consequent potential for addictive behavior. Eligibility for participation in this study was based on the following criteria: participants were required to be currently enrolled full-time undergraduate students within the age range of 19–30 years. Furthermore, participants were required to exhibit active engagement with at least one Facebook, indicated by daily logins or frequent activity on the platform. Prospective participants failing to meet these established criteria were excluded from the study.

Facebook was selected as the platform of interest for this study due to several reasons. First, as of our knowledge cutoff in March 2023, Facebook remains one of the most popular and widely used SNSs globally, with billions of monthly active users 79 . This extensive user base increases the likelihood of obtaining a representative and diverse sample for the study, enhancing the external validity of our findings. Second, Facebook's comprehensive features, ranging from text updates, image and video sharing, livestreaming, private messaging, groups, events, to various interactive activities, make it a robust platform for studying diverse user behaviors. The variety of tools and features on Facebook may contribute to a higher risk of addictive behaviors as users have multiple avenues for engagement. Last, previous research on SNS addiction has frequently used Facebook as the platform for study due to its popularity and diverse user demographic. This consistency allows for easier comparison of results across different studies, thereby contributing to a more cohesive body of literature on SNS addiction.

In advance of participant recruitment, an a priori power analysis was conducted using DanielSoper calculation 80 . The results indicated a necessary minimum sample size of 200 participants for achieving an alpha level of 0.05 and a power of 0.80, thus ensuring the detection of medium-sized effects. In order to secure a representative sample, we employed a stratified sampling technique, taking care to ensure adequate representation from a variety of institutions, faculties, and academic years. An electronic questionnaire was disseminated through a range of online platforms and college forums, with a clear emphasis on informed consent and guaranteed anonymity of the respondents.

Data collection was carried out over a period of two months. During the distribution of the questionnaire, we collaborated with several professors who graciously assisted with the sampling, allowing us to target a diverse group of students from various majors and academic years. Additionally, we disseminated the survey through online communities and portals frequented by university students. By using these methods, we gathered data from a broad range of university student respondents. The instruments in questionnaire incorporated sections dedicated to the collection of demographic data, SNS usage patterns, and specific psychological variables of interest including affect, flow, habit, perceived enjoyment, and levels of SNS addiction. Following the collection of data, analysis was conducted using SPSS software. Descriptive statistics were initially employed to provide a summary overview of the collected data, with subsequent inferential statistical analyses performed to test the established research hypotheses. A total of 282 responses were used for the final analysis. Table 1 presents the demographic characteristics of the study's respondents. The total sample consisted of 282 participants. Regarding gender distribution, 127 participants (45.0%) identified as male, while 155 participants (55.0%) identified as female. This indicates a slightly higher representation of female respondents in the study. The age distribution among the respondents was grouped into three categories: those in their teens (10s), twenties (20s), and thirties (30s). A significant proportion of the participants fell within the teen and twenties age categories, with each constituting 32.3% and 35.8% of the total respondents respectively. Participants in their thirties made up the smallest age group, with 90 respondents (31.9%). It should be noted, however, that these percentages overlap due to the significant number of individuals in their early twenties who are also technically in their late teens. In summary, the respondent profile reflects a diverse and representative sample of individuals with varied demographic characteristics, which strengthens the generalizability of the study's findings.

Measurement instrument

Table 2 presents the definitions of the primary research variables utilized in the study. Positive affect and negative affect are defined based on the emotional reactions users experience when engaging with SNS, and they are sourced from Beatty and Ferrell 81 . Social influence represents the perception of the need to use SNS to stay current or due to recommendations, as identified by Li 71 . Flow, sourced from Gong et al. 61 , describes the immersive and singularly focused state of a user on SNS activities. Davis et al. 74 provides the definition for perceived enjoyment, emphasizing the pleasure and interest users derive from SNS. Habit, according to Limayem et al. 26 , is characterized by a consistent and unconscious tendency to use SNS during leisure or to alleviate boredom. Lastly, Addiction captures the intense involvement in SNS, leading to diminished real-world social interactions and a decrease in positive emotions, as described by Osatuyi and Turel 30 .

In measuring constructs in this study, all questions were adapted from previously validated studies in the information systems and social networking services fields. These items were modified to fit the context of SNS use. All indicators were assessed using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The measurement of SNS addiction was done using self-reporting scales, comprising various items that gauge the frequency, intensity, and the negative implications of SNS use. In our research, we use the following items Osatuyi and Turel 30 : "I was immersed in SNS and experienced a decrease in conversations when meeting people.", "As I used SNS, the affectionate emotions of the past decreased." Respondents rate these statements on a scale, commonly ranging from "strongly disagree" to "strongly agree." The specific measurement items and corresponding references of all constructs can be found in Table A1 .

Prior to deployment, the questionnaire underwent a rigorous review by two information systems researchers to address content relevance and question ambiguity. Moreover, a pilot test was conducted to examine the clarity, comprehension, and applicability of the survey items. This preliminary study involved 20 participants, representative of our target population. The participants were requested to provide feedback regarding the understandability, relevance, and any ambiguity related to the survey questions. Based on their feedback, minor adjustments were made to improve the wording and sequencing of certain questions to enhance clarity and coherence.

This study validated the measurement model and the structural model by using the partial least squares structural equation modeling (PLS-SEM) through SmartPLS 4 82 . PLS-SEM also offers some benefits in terms of fewer restrictions on sample size and residuals compared to covariance-based SEM such as AMOS and LISREL 83 , 84 .

Common method bias (CMB)

To assess the potential issue of CMB, Harman’s one-factor test was employed 85 . In the exploratory factor analysis, results indicated that the first factor explained 32.801% of the variance, which was considerably less than the threshold of 50%, suggesting that CMB was not a predominant concern in our data. Moreover, in observing the variance inflation factor (VIF) from our regression analysis, all values were well below the critical value of 10, providing further evidence against significant multicollinearity and CMB 86 .

Measurement model

This study assessed the reliability and validity of the measurement model. This research examined Cronbach's alpha and composite reliability (CR: rho_A, rho_C) to evaluate reliability. If Cronbach's alpha is over 0.60 87 and CR is greater than 0.7 88 , reliability is achieved. As shown in Table 3 , Cronbach’s alpha and CR scores of all the constructs, except for social influence, exceeded the recommended value. Nevertheless, this study decided to retain social influence as other estimates such as CR (rho_C) and AVE were well above the recommended threshold (0.824 and 0.702, respectively).

This study investigated convergent validity and discriminant validity to evaluate the validity. Convergent validity was confirmed by investigating both the average variance extraction (AVE) and the factor loads of the items associated with each construct. AVE values ranged between 0.702 and 0.899 which are higher than the expected threshold of 0.5 88 . Factor loadings ranged from 0.765 to 0.964 and are all statistically significant at the p = 0.001 levels, supporting that the model has a satisfactory level of convergent validity 89 .

Discriminant validity ensures that a construct is indeed distinct from other constructs by empirical standards 88 . For the present study, two criteria were utilized to determine discriminant validity. Firstly, the square root of the average variance extracted (AVE) for each construct was compared against its correlations with other constructs. As presented in Table 4 , the diagonal values (which are the square root of the AVE) for each construct are greater than the off-diagonal values in their respective rows and columns. This demonstrates that the constructs share more variance with their indicators than they do with any other construct, thus meeting the criteria recommended by Fornell and Larcker 88 .

Additionally, the heterotrait-monotrait ratio of correlations (HTMT) was utilized to assess discriminant validity. As suggested by the literature, an HTMT value below 0.85 indicates the presence of discriminant validity 90 . Table 5 provides the HTMT ratios for all constructs. It's clear from the values that all the ratios are below the 0.85 threshold, further confirming the discriminant validity of our constructs. In conclusion, both the square root of AVE and HTMT criteria confirm the discriminant validity of the constructs utilized in this study.

Moreover, HTMT assessment was carried out using bootstrapping with 5000 samples. All 95% confidence intervals substantially veer away from the null value of 1, thereby attesting to discriminant validity (the most elevated value observed is 0.713 between perceived enjoyment and habit; all other pairs showcase values of 0.691 or lower). At the 99% confidence intervals, the upper boundary for the pair perceived enjoyment-habit increases to 0.741, with the subsequent highest value being 0.729.

Structural model

The hypotheses were tested by using the PLS-SEM technique. This study carried out bootstrapping resampling method with 5000 re-samples. Ten of the fourteen hypotheses in the research framework are supported. Figure 2 shows the analysis results.

Analysis results.

As proposed, positive affect has a significant positive influence on both flow (b = 0.298, t = 3.709) and perceived enjoyment (b = 0.515, t = 11.56), strongly supporting H1a and H1b. Contrary to expectations, positive affect does not impact habit (b = − 0.029, t = 0.435), failing to confirm H1c. As anticipated, negative affect has a significant correlation with both flow (b = 0.239, t = 3.456) and perceived enjoyment (b = − 0.341, t = 6.907), thereby supporting H2a and H2b. Conversely, negative affect does not have an impact on habit (b = 0.101, t = 1.592), failing to support H2c. Contrary to expectations, social influence does not correlate with the flow (b = 0.059, t = 1.017), and thus, H3a is not supported. In line with the hypothesis, social influence significantly influences both perceived enjoyment (b = 0.231, t = 3.759) and habit (b = 0.214, t = 3.75), which validates H3b and H3c. As expected, flow is significantly associated with addiction (b = 0.225, t = 3.643), confirming H4. Perceived enjoyment, as hypothesized, exerts a significant positive effect on both flow (b = 0.15, t = 2.476) and habit (b = 0.496, t = 8.199), robustly supporting H5a and H5b. Contradictory to expectations, perceived enjoyment does not influence addiction (b = − 0.056, t = 0.675), failing to confirm H5c. In line with the hypothesis, habit is significantly linked to addiction (b = 0.229, t = 3.09), thereby supporting H6. Overall, the conceptual framework accounted for approximately 11.6% of the variance in addiction. Table 6 shows the summary of the SEM results.

The effect size (f 2 ) provides an estimation of the magnitude of the difference or the effect of one variable on another. Cohen 91 suggests guidelines for assessing the size of f 2 effects: small (0.02), medium (0.15), and large (0.35). Upon evaluation, the computed f 2 values for our relationships range from 0.001 to 0.320. Specifically, the influence of positive affect on perceived enjoyment exhibits a large effect size (f 2 = 0.320). The impacts of positive affect on flow and habit show small effect sizes, with f 2 values of 0.072 and 0.001 respectively. The effects of negative affect on perceived enjoyment, flow, and habit are 0.154, 0.059, and 0.012 respectively, indicating small to medium effect sizes. The influences of social influence on perceived enjoyment, flow, and habit have f 2 values of 0.074, 0.004, and 0.059 respectively, all suggesting small effect sizes. Lastly, the effects of perceived enjoyment on flow, habit, and addiction are 0.020, 0.244, and 0.002 respectively. Additionally, flow and habit on addiction have effect sizes of 0.051 and 0.040 respectively. These too suggest small to medium effect sizes. It is evident that not all f 2 values meet the recommended thresholds. The low effect size in certain relationships may suggest that other unconsidered variables or intricate interplays may be at work, which are not captured by the current model. Further research might be necessary to delve deeper into these findings.

The Q 2 value is an indicator of the predictive relevance of the endogenous latent variables in the model 92 . In PLS-SEM, a Q 2 value greater than zero indicates that the exogenous constructs have predictive relevance for the endogenous constructs 92 . In assessing the model's predictive power, perceived Enjoyment revealed a Q 2 value of 0.341, aligned with RMSE and MAE values of 0.819 and 0.634, signifying moderate predictive capacity. Flow has a Q 2 of 0.245 with RMSE and MAE values at 0.876 and 0.698, indicating modest predictive relevance. Habit demonstrates a Q 2 of 0.174, associated with RMSE and MAE metrics of 0.917 and 0.707, underscoring satisfactory predictive accuracy. However, addiction records a Q 2 of 0.089 with RMSE and MAE at 0.961 and 0.804, suggesting a limited predictive scope in the present configuration.

Ethical approval

This study was approved by an institutional review board of HJ Institute of Technology and Management.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Consent to participate

Consent to participate was obtained from all individual participants included in the study.

Our investigation has unearthed certain significant insights that illuminate unique facets of SNS behavior and provide nuanced interpretations that expand upon the extant literature.

The analysis revealed that positive affect influences flow and perceived enjoyment, which aligns with previous research suggesting a significant relationship between positive affect and flow 65 , 66 , and perceived enjoyment 13 . This suggests that when users are in a better mood, they tend to immerse themselves more in social networking and enjoy it more. However, although Weyland et al. asserted a significant link between positive affect and habit 93 , this study found no such influence. The discrepancy between this study and Weyland's could be explained by the suggestion that positive affect allows users to use SNS consciously to some degree, due to the self-regulation improving characteristics of positive affect after ego depletion 94 .

Similarly, the results indicated that negative affect impacts flow and perceived enjoyment, echoing the findings of prior research 13 , 95 . This implies that users who experience negative moods are more likely to immerse themselves in SNS while deriving less enjoyment from it. Contrary to expectations, negative affect did not influence habit, suggesting that negative affect does not contribute to the formation of habitual use. It seems that negative emotions, such as distress and aversive mood states, do not necessarily lead to the development of habitual SNS usage. A plausible inference could be that users experiencing negative affect might engage with SNS more consciously or irregularly, trying to alleviate negative moods through certain activities or interactions. This conscious, irregular usage pattern might not provide the consistent reinforcement needed to form a habit. Furthermore, it is also possible that individuals experiencing negative affect might be more likely to engage in a broader range of coping mechanisms, not limited to SNS usage, which would dilute the potential for habit formation on SNS. Lastly, this finding could imply that negative affect might result in less pleasurable or satisfying SNS experiences, which are essential for habit formation. Since habits are often formed when an activity provides consistent satisfaction or reward, if negative affect reduces the perceived reward from using SNS, it could potentially inhibit habit formation.

The findings underscored that social influence significantly affects perceived enjoyment and habit, corroborating previous studies 96 , 97 , 98 , 99 . This suggests that users who are more influenced by their social environment find more enjoyment in SNS and use it more habitually. Yet, the absence of a direct relationship with flow offers a fresh dimension: flow, an inherently personal psychological state, appears to be insulated from the sway of social influences. In the context of SNS usage, this might infer that while social influences can shape the manner in which a user engages with the platform (e.g., frequency of use, types of activities participated in), it doesn't necessarily dictate the depth of immersion or the quality of the user's engagement. A user might be influenced by their social circle to use an SNS platform or adopt certain behaviors on it, but whether they achieve a state of flow while using the platform could be more dependent on individual factors such as personal interests, the appeal of the platform's features to the user, or their mood at the time of use. This finding underscores the need for further research to fully understand the interplay between social influence and flow in the context of SNS usage, particularly exploring the influence of individual user characteristics and motivations.

Our study confirmed that flow influences addiction, aligning with existing literature 32 , 33 , 62 , suggesting that a higher level of user immersion could lead to greater addiction. The findings could be understood through the framework of how flow experiences can engender repeated and escalated use of a platform, potentially leading to compulsive and addictive behaviors. In the context of SNS usage, a flow state might occur when users are so absorbed in browsing posts, interacting with others, or creating content that they lose track of time and are oblivious to external distractions. When users frequently experience flow while using an SNS platform, it can lead to higher levels of satisfaction and enjoyment. These positive experiences can, in turn, motivate them to seek out repeated instances of such experiences, potentially leading to more frequent and prolonged use of the platform. Over time, this escalated use can develop into habit formation, and in some cases, escalate into addiction. This is particularly likely if the SNS platform becomes a primary source of positive reinforcement or pleasure for the user, or if the user becomes reliant on the platform to escape negative feelings or realities.

The study found that perceived enjoyment impacts flow and habit, which is consistent with previous studies 10 , 100 . This implies that users who derive more enjoyment from SNS are more likely to immerse themselves in its usage and develop a habit. However, contrary to some previous research 9 , 76 , 77 , our findings showed that perceived enjoyment is not related to addiction, suggesting that factors other than perceived enjoyment might have a more significant impact on SNS addiction.

Finally, our analysis revealed that habit significantly impacts addiction, in agreement with prior findings 10 , 30 . This suggests that habitual SNS users are more prone to developing an addiction. Habit may be represented by frequent checking of notifications, constant scrolling of feeds, or habitual posting of updates, among other behaviors. When these behaviors become ingrained, they form a pattern that requires minimal cognitive effort to initiate. The repeated engagement with the SNS platform may then become an automatic response to certain triggers, such as a notification sound or even a moment of boredom. In the early stages, these habits may simply represent high engagement with the platform. However, over time and with continued repetition, these habitual behaviors can lead to dependence and eventually addiction. This is particularly likely when the habitual use of the platform is paired with strong positive reinforcement (e.g., receiving likes or comments) or serves as a coping mechanism for negative emotions. Addiction, in this context, is characterized by an inability to control SNS usage despite negative consequences, a preoccupation with the SNS platform, and potential distress or withdrawal symptoms when access to the platform is denied or limited. Thus, the development of strong, unconscious SNS usage habits could significantly contribute to the onset of such addictive behaviors.

This study illuminated the precursors of SNS addiction, highlighting flow, habits, and perceived enjoyment as pivotal determinants. The research suggested that positive affect, negative affect, and social influence were central contributors to the onset of addiction and its precursors. A survey of SNS users was carried out, and the PLS-SEM methodology was employed to evaluate both the measurement and structural models. The findings demonstrated that positive affect significantly impacted both flow and perceived enjoyment, while negative affect notably influenced flow and perceived enjoyment. Additionally, social influence significantly affected perceived enjoyment and habit. The results underscored that perceived enjoyment influenced addiction significantly through both flow and habit.

Theoretical implications

This study presents several significant academic contributions. Firstly, it enriches the SNS literature by introducing a comprehensive model that incorporates flow, habit, and perceived enjoyment to explain SNS addiction among users. While previous studies on SNS addiction have primarily focused on health, loneliness, and attachment 17 , 52 , 101 , our research intricately weaves these concepts into a coherent framework, shedding light on their combined and individual roles in driving SNS addiction. The cascading effect delineated in this research, tracing the journey from perceived enjoyment to addiction through the intricate interplay of flow and habituation, heralds a groundbreaking perspective in SNS addiction studies. This view not only challenges the dominant linear frameworks often seen in the literature but also introduces a much-needed nuanced understanding. The triangulated perspective brought forth in this work goes beyond merely linking variables; it unravels the depth of their interconnections, offering scholars a comprehensive lens that is markedly distinct from the fragmented approaches seen in prior works. Its novelty lies in its multi-dimensional approach, capturing the essence of intertwined variables rather than isolating them. For scholars, this presents an invitation to delve deeper into understanding the complexities of SNS addiction, and, more broadly, prompts a reconsideration of how other behavioral addictions might be driven by a synergy of factors rather than linear paths. Such insights pave the way for richer, more detailed investigations in the future, shaping the trajectory of addiction research.

Secondly, this research takes a notable step further by integrating a dualistic scale of users’ affect—both positive and negative—into the fabric of our understanding of SNS addiction. This bifurcation in affective states offers a fresh lens to comprehend the intricacies of user engagement with SNS platforms. A particularly arresting observation from our study is the role of these affective dimensions in modulating the 'flow' within SNS engagements. Both positive and negative affects emerged as potent enhancers of flow. This illuminates an intriguing facet of user behavior: irrespective of whether users are in a state of contentment or grappling with emotional discomfort, they exhibit a heightened propensity to lose themselves in the labyrinth of SNS activities. The underlying reasons for this phenomenon could be multifaceted. Users might be navigating the SNS realm in search of vicarious contentment by witnessing the seemingly euphoric lives of their peers, especially during their own moments of distress. Alternatively, some might be on a quest to find peers who portray lives perceived as less joyous, perhaps as a means to assuage their own emotional tumult. Yet another possibility is the unintentional immersion in SNS activities as a coping mechanism against feelings of relative deprivation. It's noteworthy that while these affective states profoundly influence flow, they remain inconsequential in forming habits, suggesting a discernible boundary between transient emotions and ingrained behaviors. The differential impact of these emotional scales on perceived enjoyment, aligning with the theoretical assertions of their orthogonal relationship 25 , further enriches our comprehension of the complex tapestry of SNS engagement dynamics.

Lastly, this study probes the role of social influence in the process of addiction formation. Given that the unique function of SNS is fostering social relationships, the mutual influence among users can significantly impact SNS activity. Yet, the analysis shows that social influence does not directly precipitate addiction or flow. This indicates that intrinsic motivations might be stronger than extrinsic factors in fostering addiction and flow. This finding provides a useful perspective for future studies on SNS addiction, suggesting a systematic classification of internal and external explanatory factors. Social influence significantly affects habits and perceived enjoyment, indicating that users are more likely to use SNS habitually and enjoy it more when they frequently hear about it from people around them. However, addiction and flow seem to require a higher level of concentration than mere habit formation. Typically, SNS users engage with the platforms for light hobbies or social interactions, rather than work or academic purposes. Against this backdrop, it appears that even when influenced by their surroundings, SNS users primarily maintain a habitual level of engagement.

Practical implications

This paper presents various practical implications. Firstly, habit is a primary factor in SNS addiction. Thus, users should be guided to avoid excessive SNS usage to prevent addiction. Strategies like limiting usage or curtailing frequent app access can aid addicted individuals 102 . Secondly, the study underscores perceived enjoyment's significant impact on flow and habit. SNS providers might intersperse public interest messages within feeds to reduce habit formation and potential addiction. Thirdly, the data reveals positive affect's substantial role in addiction, flow, and enjoyment. It's vital for developers to balance positive affect's enhancement of user experience without inducing addiction. Fourthly, despite negative feelings, users engage with SNS. Understanding their motives can help service providers foster healthier interactions and mitigate negative emotions. Lastly, the influence of social word-of-mouth bolsters habits and enjoyment in SNS. Providers can amplify engagement by incentivizing users to share platform benefits.

Limitation and further research

Despite several contributions, this study also has some limitations. First, the study was conducted exclusively in South Korea, limiting its geographical scope. SNS addiction could potentially vary according to culture, nationality, and the maturity of online social networks across different nations. Therefore, future research could enhance the generalizability of the results by investigating user behaviors relating to addiction in various countries. Second, this paper examined SNS addiction solely among Facebook users. However, user engagement and addiction behavior might differ based on the platform's structure or operation methods. Consequently, future studies should consider obtaining samples from users of other platforms such as Pinterest, Twitter, YouTube, and Instagram. This would allow for a deeper exploration of how user addictions form, in relation to the distinct characteristics of each SNS.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Agadullina, E. R., Lovakov, A. & Kiselnikova, N. V. Does quitting social networks change feelings of loneliness among freshmen? An experimental study. J. Appl. Res. High Educ. 13 , 149–163 (2020).

Wheatley, D. & Buglass, S. L. Social network engagement and subjective well-being: A life-course perspective. Br. J. Sociol. 70 , 1971–1995 (2019).

Article PubMed Google Scholar

Yokoyama, M. H. How social network sites (SNS) have changed the employer–employee relationship and what are the next challenges for human resource (HR)?. REGE-Rev. Gest. 23 , 2–9 (2016).

Google Scholar

Chen, H. & Chen, H. Understanding the relationship between online self-image expression and purchase intention in SNS games: A moderated mediation investigation. Comput. Hum. Behav. 112 , 106477 (2020).

Article Google Scholar

Kümpel, A. S. The Matthew effect in social media news use: Assessing inequalities in news exposure and news engagement on social network sites (SNS). Journalism 21 , 1083–1098 (2020).

Pujadas-Hostench, J., Palau-Saumell, R., Forgas-Coll, S. & Matute, J. Integrating theories to predict clothing purchase on SNS. Ind. Manag. Data Syst. 119 , 1015 (2019).

BroadbandSearch. Average Time Spent Daily on Social Media (Lastest 2022 Data), https://www.broadbandsearch.net/blog/average-daily-time-on-social-media (2023).

BusinessofApps. Most Popular Apps (2021) , https://www.businessofapps.com/data/most-popular-apps/ (2021).

Kim, E., Libaque-Saenz, C. F. & Park, M.-C. Understanding shopping routes of offline purchasers: Selection of search-channels (online vs. offline) and search-platforms (mobile vs. PC) based on product types. Serv. Bus. 13 , 305–338 (2019).

Yang, S., Wang, B. & Lu, Y. Exploring the dual outcomes of mobile social networking service enjoyment: The roles of social self-efficacy and habit. Comput. Hum. Behav. 64 , 486–496. https://doi.org/10.1016/j.chb.2016.07.010 (2016).

Andreassen, C. S. Online social network site addiction: A comprehensive review. Current Addict. Rep. 2 , 175–184 (2015).

Yin, L. et al. Social networking sites addiction and FoMO: The mediating role of envy and the moderating role of need to belong. Current Psychol. 40 , 3879–3887. https://doi.org/10.1007/s12144-019-00344-4 (2021).

Jo, H. Antecedents of continuance intention of social networking services (SNS): Utilitarian, hedonic, and social contexts. Mob. Inf. Syst. 2022 , 7904124. https://doi.org/10.1155/2022/7904124 (2022).

Jo, H. Effects of psychological discomfort on social networking site (SNS) usage intensity during COVID-19. Front. Psychol. 13 , 939726. https://doi.org/10.3389/fpsyg.2022.939726 (2022).

Article PubMed PubMed Central Google Scholar

Ünal-Aydın, P., Balıkçı, K., Sönmez, İ & Aydın, O. Associations between emotion recognition and social networking site addiction. Psychiatry Res. 284 , 112673. https://doi.org/10.1016/j.psychres.2019.112673 (2020).

Wang, C.-W., Ho, R. T., Chan, C. L. & Tse, S. Exploring personality characteristics of Chinese adolescents with internet-related addictive behaviors: Trait differences for gaming addiction and social networking addiction. Addict. Behav. 42 , 32–35 (2015).

Article CAS PubMed Google Scholar

Zhou, S. X. Gratifications, loneliness, leisure boredom and self-esteem as predictors of sns-game addiction and usage pattern among Chinese college students. The Chinese University of Hong Kong, Master Thesis (2010).

Kuss, D. J. & Griffiths, M. D. Social networking sites and addiction: Ten lessons learned. Int. J. Environ. Res. Public Health 14 , 311 (2017).

Primack, B. A. et al. Social media use and perceived social isolation among young adults in the US. Am. J. Prev. Med. 53 , 1–8 (2017).

Woods, H. C. & Scott, H. # Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J. Adolesc. 51 , 41–49 (2016).

Arnett, J. J. Emerging adulthood: A theory of development from the late teens through the twenties. Am. Psychol. 55 , 469 (2000).

Kuss, D. J., Griffiths, M. D., Karila, L. & Billieux, J. Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharm. Des. 20 , 4026–4052 (2014).

Article CAS Google Scholar

Franchina, V., Vanden Abeele, M., Van Rooij, A. J., Lo Coco, G. & De Marez, L. Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. Int. J. Environ. Res. Public Health 15 , 2319 (2018).

Lee, J., Kim, B. & Jo, H. Understanding post-adoption behavior of SNS users. Telecommun. Rev. 24 , 121–136 (2014).

Watson, D., Clark, L. A. & Tellegen, A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J. Personal. Soc. Psychol. 54 , 1063 (1988).

Limayem, M., Hirt, S. G. & Cheung, C. M. How habit limits the predictive power of intention: The case of information systems continuance. MIS Q. 31 , 705–737 (2007).

Kardefelt-Winther, D. A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Comput. Hum. Behav. 31 , 351–354 (2014).

Hussong, A. M. Predictors of drinking immediacy following daily sadness: An application of survival analysis to experience sampling data. Addict. Behav. 32 , 1054–1065 (2007).

Wang, Y., Meister, D. B. & Gray, P. H. Social influence and knowledge management systems use: Evidence from panel data. Mis Q. 37 , 299–313 (2013).

Osatuyi, B. & Turel, O. Tug of war between social self-regulation and habit: Explaining the experience of momentary social media addiction symptoms. Comput. Hum. Behav. 85 , 95–105 (2018).

Csikszentmihalyi, M. Flow. The Psychology of Optimal Experience (HarperPerennial, 1990).

Chou, T.-J. & Ting, C.-C. The role of flow experience in cyber-game addiction. CyberPsychol. Behav. 6 , 663–675 (2003).

Sun, Y., Zhao, Y., Jia, S.-Q. & Zheng, D.-Y. “Understanding the Antecedents of Mobile Game Addiction: The Roles of Perceived Visibility, Perceived Enjoyment and Flow”. in Proceedings of the 19th Pacific-Asia Conference on Information Systems, Marian Bay Sands, Singapore, 1-12 (2015).

Jun, S. & Choi, E. Academic stress and internet addiction from general strain theory framework. Comput. Hum. Behav. 49 , 282–287 (2015).

Deci, E. L. & Ryan, R. M. The general causality orientations scale: Self-determination in personality. J. Res. Personal. 19 , 109–134 (1985).

Turel, O. & Serenko, A. The benefits and dangers of enjoyment with social networking websites. Eur. J. Inf. Syst. 21 , 512–528 (2012).

Ha, I., Yoon, Y. & Choi, M. Determinants of adoption of mobile games under mobile broadband wireless access environment. Inf. Manag. 44 , 276–286 (2007).

Jo, H. Key factors influencing loyalty and satisfaction toward ERP: mediating role of flow. J. Knowl. Econ. https://doi.org/10.1007/s13132-022-00981-4 (2022).

Verplanken, B. & Aarts, H. Habit, attitude, and planned behaviour: Is habit an empty construct or an interesting case of goal-directed automaticity?. Eur. Rev. Soc. Psychol. 10 , 101–134 (1999).

LaRose, R. The problem of media habits. Commun. Theory 20 , 194–222 (2010).

Turel, O., He, Q., Xue, G., Xiao, L. & Bechara, A. Examination of neural systems sub-serving Facebook “addiction”. Psychol. Rep. 115 , 675–695 (2014).

Griffiths, M. D., Kuss, D. J. & Demetrovics, Z. Behavioral addictions: Criteria, evidence and treatment. In Social networking addiction: An overview of preliminary findings. Rosenber, K., Feder, L., Eds.; Elsevier Inc.: New York, NY, USA, 119–141 (2014).

Blackwell, D., Leaman, C., Tramposch, R., Osborne, C. & Liss, M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personal. Individ. Differ. 116 , 69–72 (2017).

Scott, E. S., Canivet, C. & Östergren, P.-O. Investigating the effect of social networking site use on mental health in an 18–34 year-old general population; a cross-sectional study using the 2016 Scania Public Health Survey. BMC Public Health 20 , 1753. https://doi.org/10.1186/s12889-020-09732-z (2020).

Cunningham, S., Hudson, C. C. & Harkness, K. Social media and depression symptoms: A meta-analysis. Res. Child Adolesc. Psychopathol. 49 , 241–253. https://doi.org/10.1007/s10802-020-00715-7 (2021).

Bottaro, R. & Faraci, P. The use of social networking sites and its impact on adolescents’ emotional well-being: A scoping review. Current Addict. Rep. 9 , 518–539. https://doi.org/10.1007/s40429-022-00445-4 (2022).

Ellison, N. B., Steinfield, C. & Lampe, C. The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. J. Comput.-Mediat. Commun. 12 , 1143–1168 (2007).

Nabi, R. L., Prestin, A. & So, J. Facebook friends with (health) benefits? Exploring social network site use and perceptions of social support, stress, and well-being. Cyberpsychol. Behav. Soc. Netw. 16 , 721–727 (2013).

Kross, E. et al. Facebook use predicts declines in subjective well-being in young adults. PloS ONE 8 , e69841 (2013).

Article CAS PubMed PubMed Central ADS Google Scholar

Verduyn, P. et al. Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. J. Exp. Psychol. Gen. 144 , 480 (2015).

Vogel, E. A., Rose, J. P., Roberts, L. R. & Eckles, K. Social comparison, social media, and self-esteem. Psychol. Pop. Media Cult. 3 , 206 (2014).

Monacis, L., De Palo, V., Griffiths, M. D. & Sinatra, M. Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. J. Behav. Addict. 6 , 178–186 (2017).

Park, K. & Oh, Y. Structural relationship of factors affecting SNS addiction in pre-service teachers. NVEO-Nat. Volatiles Essent. Oils J. 8 , 1002–1011 (2021).

Deutsch, M. & Gerard, H. B. A study of normative and informational social influences upon individual judgment. J. Abnorm. Soc. Psychol. 51 , 629–636. https://doi.org/10.1037/h0046408 (1955).

Tandoc, E. C. Jr., Ferrucci, P. & Duffy, M. Facebook use, envy, and depression among college students: Is facebooking depressing?. Comput. Hum. Behav. 43 , 139–146 (2015).

Nesi, J. & Prinstein, M. J. Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms. J. Abnorm. Child Psychol. 43 , 1427–1438 (2015).

Chen, H., Wigand, R. T. & Nilan, M. Exploring web users’ optimal flow experiences. Inf. Technol. People 13 , 263–281 (2000).

Faiola, A., Newlon, C., Pfaff, M. & Smyslova, O. Correlating the effects of flow and telepresence in virtual worlds: Enhancing our understanding of user behavior in game-based learning. Comput. Hum. Behav. 29 , 1113–1121 (2013).

LaRose, R., Lin, C. A. & Eastin, M. S. Unregulated internet usage: Addiction, habit, or deficient self-regulation?. Media Psychol. 5 , 225–253 (2003).

Kim, B.-S. & Kim, D.-K. A study on antecedents of SNS user’s addiction in the age of convergence: The role of SNS habit. J. Digit. Converg. 13 , 161–168 (2015).

Gong, M., Xu, M., Luqman, A., Yu, L. & Masood, A. Understanding the role of individual differences in mobile SNS addiction. Kybernetes 49 , 3069 (2020).

Seo, D. & Ray, S. Habit and addiction in the use of social networking sites: Their nature, antecedents, and consequences. Comput. Hum. Behav. 99 , 109–125 (2019).

Pontes, H. M., Taylor, M. & Stavropoulos, V. Beyond, “Facebook addiction”: The role of cognitive-related factors and psychiatric distress in social networking site addiction. Cyberpsychol. Behav. Soc. Netw. 21 , 240–247 (2018).

Griffiths, M. D. Social networking addiction: Emerging themes and issues. J. Addict. Res. Therapy https://doi.org/10.4172/2155-6105.1000e118 (2013).

Chen, H. Flow on the net-detecting Web users’ positive affects and their flow states. Comput. Hum. Behav. 22 , 221–233. https://doi.org/10.1016/j.chb.2004.07.001 (2006).

Rogatko, T. P. The Influence of Flow on Positive Affect in College Students (University of Maryland, 2007).

Islam, A. K. M. N., Mäntymäki, M., Laato, S. & Turel, O. Adverse consequences of emotional support seeking through social network sites in coping with stress from a global pandemic. Int. J. Inf. Manag. 62 , 102431. https://doi.org/10.1016/j.ijinfomgt.2021.102431 (2022).

Lee, D., Moon, J., Kim, Y. J. & Yi, M. Y. Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty. Inf. Manag. 52 , 295–304. https://doi.org/10.1016/j.im.2014.12.001 (2015).

Rice, R. E., Grant, A. E., Schmitz, J. & Torobin, J. Individual and network influences on the adoption and perceived outcomes of electronic messaging. Soc. Netw. 12 , 27–55 (1990).

Xia, J., Wu, T. & Zhou, L. Sharing of verified information about covid-19 on social Network sites: A social Exchange theory perspective. Int. J. Environ. Res. Public Health 18 , 1260 (2021).

Li, D. C. Online social network acceptance: a social perspective. Internet Res. 21 , 562 (2011).

Article ADS Google Scholar

Csikszentmihalyi, M. & Csikzentmihaly, M. Flow: The Psychology of Optimal Experience Vol. 1990 (Harper, 1990).

Khang, H., Kim, J. K. & Kim, Y. Self-traits and motivations as antecedents of digital media flow and addiction: The Internet, mobile phones, and video games. Comput. Hum. Behav. 29 , 2416–2424 (2013).

Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. Extrinsic and intrinsic motivation to use computers in the workplace 1. J. Appl. Soc. Psychol. 22 , 1111–1132 (1992).

Kim, J.-H., Kim, G.-J., Choi, H.-J., Seok, B.-I. & Lee, N.-H. Effects of social network services (SNS) subjective norms on SNS addiction. J. Psychol. Africa 29 , 582–588. https://doi.org/10.1080/14330237.2019.1694735 (2019).

Sheshadri, C. Impact on addiction of online platforms on quality of life: Age and Gender as moderators. Australas. J. Inf. Syst. https://doi.org/10.3127/ajis.v25i0.2761 (2021).

Chatterjee, S., Chaudhuri, R., Thrassou, A. & Vrontis, D. Social network games (SNGs) addiction: Psychological dimensions and impacts on life quality and society. Technol. Forecast. Soc. Change 177 , 121529. https://doi.org/10.1016/j.techfore.2022.121529 (2022).

Triandis, H. C. Attitude and Attitude Change. Wiley Foundations of Social Psychology Series (Wiley, 1971).

Wise, J. Facebook Market Share in 2023 (U.S. & WORLD!), https://earthweb.com/facebook-market-share/ (2023).

DanielSoper.com. Free Statistics Calculators Available at https://www.danielsoper.com/statcalc/default.aspx . (accessed on 8 December 2021).

Beatty, S. E. & Ferrell, M. E. Impulse buying: Modeling its precursors. J. Retail. 74 , 169–191 (1998).

Ringle, C. M., Wende, S., & Becker, J.-M. SmartPLS 4, http://www.smartpls.com . (2022).

Chin, W. W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295 , 295–336 (1998).

Hair, J. F., Sarstedt, M., Ringle, C. M. & Mena, J. A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40 , 414–433. https://doi.org/10.1007/s11747-011-0261-6 (2012).

Harman, H. H. Modern Factor Analysis (University of Chicago press, 1976).

MATH Google Scholar

Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C. M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31 , 2–24. https://doi.org/10.1108/EBR-11-2018-0203 (2019).

Churchill, G. A. Jr. A paradigm for developing better measures of marketing constructs. J. Mark. Res. 16 , 64–73 (1979).

Fornell, C. & Larcker, D. F. Evaluating structural equation models with unobservable variables and measurement Error. J. Mark. Res. 18 , 39–50. https://doi.org/10.2307/3151312 (1981).

Bagozzi, R. P., Yi, Y. & Phillips, L. W. Assessing construct validity in organizational research. Adm. Sci. Q. 36 , 421–458. https://doi.org/10.2307/2393203 (1991).

Henseler, J., Ringle, C. M. & Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43 , 115–135 (2015).

Cohen, J. Statistical Power Analysis (Erlbaum, 1988).

Hair, J. F. Jr., Howard, M. C. & Nitzl, C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J. Bus. Res. 109 , 101–110 (2020).

Weyland, S., Finne, E., Krell-Roesch, J. & Jekauc, D. (How) Does affect influence the formation of habits in exercise?. Front. Psychol. 11 , 2866 (2020).

Tice, D. M., Baumeister, R. F., Shmueli, D. & Muraven, M. Restoring the self: Positive affect helps improve self-regulation following ego depletion. J. Exp. Soc. Psychol. 43 , 379–384. https://doi.org/10.1016/j.jesp.2006.05.007 (2007).

Tobert, S. & Moneta, G. B. Flow as a function of affect and coping in the workplace. Individ. Differ. Res. 11 , 102 (2013).

Joe, S., Kim, J. & Zemke, D. M. V. Effects of social influence and perceived enjoyment on Kiosk acceptance: A moderating role of gender. Int. J. Hosp. Tour. Adm. 23 , 289–316. https://doi.org/10.1080/15256480.2020.1746217 (2022).

Koenig-Lewis, N., Marquet, M., Palmer, A. & Zhao, A. L. Enjoyment and social influence: Predicting mobile payment adoption. Serv. Ind. J. 35 , 537–554 (2015).

Raghunathan, R. & Corfman, K. Is happiness shared doubled and sadness shared halved? Social influence on enjoyment of hedonic experiences. J. Mark. Res. 43 , 386–394. https://doi.org/10.1509/jmkr.43.3.386 (2006).

Khatimah, H., Susanto, P. & Abdullah, N. L. Hedonic motivation and social influence on behavioral intention of e-money: The role of payment habit as a mediator. Int. J. Entrep. 23 , 1–9 (2019).

Matute-Vallejo, J. & Melero-Polo, I. Understanding online business simulation games: The role of flow experience, perceived enjoyment and personal innovativeness. Australas. J. Educ. Technol. https://doi.org/10.14742/ajet.3862 (2019).

Xue, Y. et al. Investigating the impact of mobile SNS addiction on individual’s self-rated health. Internet Res. 28 , 278 (2018).

Whelan, E., Golden, W. & Tarafdar, M. How technostress and self-control of social networking sites affect academic achievement and wellbeing. Internet Res. 32 , 280–306. https://doi.org/10.1108/INTR-06-2021-0394 (2022).

Download references

The authors received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and affiliations.

Headquarters, HJ Institute of Technology and Management, Jungdong-ro 71 39, Bucheon-si, Gyeonggi-do, 14721, Republic of Korea

Department of Preventive Medicine, College of Medicine, Catholic University of Korea, Seoul, Republic of Korea

Eun-Mi Baek

You can also search for this author in PubMed Google Scholar

Contributions

H.J. wrote the original manuscript of this study. E.B. was responsible for the revision of the article. Consent to publish was obtained from all individual participants included in the study.

Corresponding authors

Correspondence to Hyeon Jo or Eun-Mi Baek .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jo, H., Baek, EM. Predictors of social networking service addiction. Sci Rep 13 , 16705 (2023). https://doi.org/10.1038/s41598-023-43796-2

Download citation

Received : 19 May 2023

Accepted : 28 September 2023

Published : 04 October 2023

DOI : https://doi.org/10.1038/s41598-023-43796-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more: https://www.cambridge.org/universitypress/about-us/news-and-blogs/cambridge-university-press-publishing-update-following-technical-disruption

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

> Handbook of Adolescent Digital Media Use and Mental Health
> Theoretical Foundations of Social Media Uses and Effects

Book contents

Handbook of Adolescent Digital Media Use and Mental Health
Copyright page
About the Editors
Contributors
Acknowledgments
Introduction
Part I Theoretical and Methodological Foundations in Digital Media Research and Adolescent Mental Health
1 Methodological and Conceptual Issues in Digital Media Research
2 Theoretical Foundations of Social Media Uses and Effects
Part III Digital Media and Adolescent Mental Disorders
Part IV Intervention and Prevention in the Digital Age

2 - Theoretical Foundations of Social Media Uses and Effects

from Part I - Theoretical and Methodological Foundations in Digital Media Research and Adolescent Mental Health

Published online by Cambridge University Press: 30 June 2022

The aim of this chapter is to discuss the communication and media effects theories that may serve as the foundations for research into the effects of social media use on adolescents. The first section of this chapter focuses on three important paradigms of general media effects theories that may help us understand the effects of social media, namely the selectivity, transactionality, and conditionality paradigms. The second section reviews computer-mediation theories, which originated in the 1970s, and are still important to understand the cognitive, affective, and behavioral effects of social media. The third section introduces a transactional affordance theory of social media uses, which is inspired by transactional theories of development (Bronfenbrenner, 2005; Sameroff, 2009), self-effects theory (Valkenburg, 2017), and affordance theories of social media use (e.g., McFarland & Ployhart, 2015). The chapter ends with some avenues for future research into the effects of social media on adolescents.

Empirical work into the cognitive, affective, and behavioral effects of media use started in the 1920s under the umbrella concept of mass communication. The term mass communication arose as a response to the new opportunities of reaching audiences via the mass media (e.g., film, radio; McQuail, Reference McQuail 2010 ). In early mass communication theories, the mass did not only refer to the “massness” of the audience that media could reach, but also to homogenous media use and powerful media effects, notions that apply increasingly less to the contemporary media landscape (Valkenburg et al., Reference Valkenburg, Peter and Walther 2016 ). In the past two decades, media use has undergone a rapid evolution. It has become increasingly individualized, and, with the introduction of social media, undeniably more dynamic and ubiquitous. It is no surprise, therefore, that communication and media effects theories have undergone important adjustments. And it is also no surprise that the mass has turned increasingly obsolete in contemporary media effects theories (Valkenburg & Oliver, Reference Valkenburg and Oliver 2019 ).

The aim of this chapter is to discuss the communication and media effects theories that may serve as the foundations for research into the effects of social media use on adolescents. To define social media, I follow the definition of Bayer et al. ( Reference Bayer, Triệu and Ellison 2020 , p. 472): Social media are “computer-mediated communication channels that allow users to engage in social interaction with broad and narrow audiences in real time or asynchronously.” Social media use thus entails the active (e.g., posting) or passive (e.g., browsing), private (one-to-one) or public (e.g., one-to-many), and synchronous or asynchronous usage of social media platforms, such as Instagram, Facebook, Snapchat, TikTok, WeChat, and WhatsApp.

The first section of this chapter focuses on three important paradigms of general media effects theories that may help us understand the effects of social media, namely the selectivity, transactionality, and conditionality paradigms. The second section reviews computer-mediated communication theories, which originated in the 1970s, and are still relevant to understand the effects of social media. The third section introduces a transactional affordance theory of social media uses, which is inspired by transactional theories of development (Bronfenbrenner, Reference Bronfenbrenner and Bronfenbrenner 2005 ; Sameroff, Reference Sameroff and Sameroff 2009 ), Self-effects theory (Valkenburg, Reference Valkenburg 2017 ), and affordance theories of social media use (e.g., boyd, Reference boyd and Papacharissi 2011 ; McFarland & Ployhart, Reference McFarland and Ployhart 2015 ). A fourth and final section presents some avenues for future research into the effects of social media on adolescents.

Media Effects Theories

In this chapter, I define media effects as the deliberate and nondeliberate short- and long-term within-person changes in cognitions, emotions, attitudes, and behavior that result from media use (Valkenburg et al., Reference Valkenburg, Peter and Walther 2016 ). And I define a (social) media effects theory as a theory that attempts to explain the uses and effects of (social) media use on individuals, groups, or societies as a whole (Valkenburg & Oliver, Reference Valkenburg and Oliver 2019 ). To be labeled a (social) media effects theory, a theory at least needs to conceptualize media use, and the potential changes that this use can bring about within individuals, groups, or societies (i.e., the media effect).

Over the past decades, dozens of media effects theories have been developed. These theories differ substantially in how they conceptualize the media effects process. Some theories, particularly the early ones, focus primarily on unidirectional linear relationships between media use and certain outcomes. Other, more comprehensive theories pay more attention to the interactive effects of media use and nonmedia factors (e.g., dispositions, social contexts) on certain outcomes. Valkenburg et al. ( Reference Valkenburg, Peter and Walther 2016 ) argued that media effects theories can be organized along five paradigms that specify the conditions under which media effects can (or cannot) occur. This chapter discusses the three paradigms that are most relevant to our understanding of the effects of social media use, the selectivity, transactionality, and conditionality paradigm. The term “message” in this chapter refers to all textual, auditory, visual, and audiovisual content that is shared on social media.

The Selectivity Paradigm

The selectivity paradigm of media effects theories states that: (a) individuals can only attend to a limited number of media messages out of the wealth of media messages that can potentially attract their attention, (b) they select these media messages in response to dispositions, needs, and desires that differ from person to person, and (c) only those media messages they select have the potential to influence them. The selectivity paradigm is represented by two different communication theories: uses and gratifications theory (Katz et al., Reference Katz, Blumler and Gurevitch 1973 ) and selective exposure theory (Zillmann & Bryant, Reference Zillmann, Bryant, Zillmann and Bryant 1985 ). Both theories argue that a variety of cognitive and psychosocial factors guide and filter one’s selective media use. An important difference between the theories is that uses and gratifications theory conceives of media users as rational and conscious of their selective media use, whereas selective exposure theory argues that media users are often not aware, or at least not fully aware, of their selection motives.

The Transactionality Paradigm

The transactionality paradigm is an extension of the selectivity paradigm. Early studies into the selectivity paradigm have predominantly focused on the extent to which the dispositions of media users (e.g., needs, moods, attitudes) predict their tendency to select media. In other words, these studies conceptualized selective media use as the outcome, whereas the effects of this media use received less attention. In more recent transactional media effects theories (e.g., Slater, Reference Slater 2007 ; Valkenburg & Peter, Reference Valkenburg and Peter 2013a ), the selectivity paradigm has become an integrated part of the media effects process. Transactional media effect theories argue that (a) the media user, rather than the media, is the starting point of a process that leads to selective media use, (b) this selective media use may bring about a transaction (i.e., change) in the media user, which is the media effect, and (c) this media effect may, in turn, reciprocally influence media use and the antecedents of media use. For example, it has been shown that adolescents high in trait aggressiveness are more likely to selectively expose themselves to violent websites, which may further enhance their trait aggressiveness (Slater, Reference Slater 2003 ).

The propositions in transactional media effects theories have important implications for theories and research on the effects of social media. First, in comparison with mass media, social media have more filters and algorithms to cater to the preferences of adolescent users, which may stimulate their selective exposure to messages that match these preferences. Second, social media platforms typically allow adolescents to make their posts more personal, vivid, and emotional, which may enhance the likelihood of effects. Third, since 2017, adolescents can not only search for messages related to a specific hashtag but can also follow one or more hashtags, after which posts under these hashtags start to show up more prominently in the users’ timelines or feeds (Scherr et al., Reference Scherr, Arendt, Frissen and Oramas 2020 ). In comparison with mass media content, such posts may be more effective both in attracting the selective attention of recipients of these posts, and in influencing their cognitions, attitudes, and behavior (e.g., Parmelee & Roman, Reference Parmelee and Roman 2020 ).

Following transactional theories, social media use may thus result in selective exposure to messages that match with individuals’ preexisting dispositions (e.g., needs, moods, attitudes), more so than mass media use. These theories thus imply that social media users may also more than mass media users be able to shape their own media effects via this targeted selective social media use. Hence, if we want to understand the effects of social media use on adolescents, we may need to study the antecedents that shape their selective social media use. Selective exposure theories have mostly focused on dispositional antecedents, such as mood and preexisting attitudes. But according to Valkenburg & Peter’s ( Reference Valkenburg and Peter 2013a ) differential susceptibility to media effects model (DSMM), three types of antecedents may predict adolescents’ selective (social) media use and, thus, the effects of this use: dispositional, developmental, and social-context factors .

Dispositional Factors

Dispositions that may lead to selective social media use range from more stable factors (e.g., temperament, personality) to more transient and situational ones (e.g., needs, desires, moods). Both types of antecedents have received some support. For example, fear of missing out (FOMO, a more stable anxiety of missing out on rewarding experiences that others are having) has been linked to adolescents’ (problematic) social media use (Franchina et al., Reference Franchina, Vanden Abeele, van Rooij, Lo Coco and De Marez 2018 ). Furthermore, some (but not all) adolescents experiencing low mood turn to social media to look for funny clips or supportive feedback (Rideout & Fox, Reference Rideout and Fox 2018 ).

Developmental Factors

As for development, research has shown that children and adolescents typically prefer media messages that are only moderately discrepant from their age-related comprehension schemata and level of psychosocial development (Valkenburg & Cantor, Reference Valkenburg, Cantor, Zillmann and Vorderer 2000 ). If they encounter media content that is too discrepant, they will allocate less attention to it or avoid it. This moderate-discrepancy hypothesis explains, for example: (a) why toddlers are typically attracted to audiovisual material with a slow pace, simple characters, and familiar contexts, and why they can be mesmerized by buttons on tablets; (b) why preschoolers typically like to attend to faster-paced, more adventurous contexts, and more sophisticated fantasy characters; (c) why children in middle childhood typically enjoy computer games and virtual worlds that allow collecting and saving, and identify with real-life idols; and (d) why adolescents are the most avid users of social media for interacting with their friends, and seek online entertainment that presents irreverent humor or risky behavior (for a more elaborate review of developmentally related media preferences, see Valkenburg and Piotrowski ( Reference Valkenburg and Piotrowski 2017 ).

Social Context Factors

Social context refers to the surroundings within which individuals or groups act or interact, and whose norms and affordances may influence the cognitions, emotions, attitudes, and behaviors that occur within it. On the macro level, structural aspects of the media system (e.g., platform availability) can affect media choices (e.g., Webster, Reference Webster and Hartmann 2009 ), whereas on the micro level, parents and schools can forbid adolescents from spending time on social media during dinner or in the classroom (Valkenburg & Piotrowski, Reference Valkenburg and Piotrowski 2017 ). In addition, especially in adolescence, peer groups can exert a strong influence on certain preferences and behaviors (Brechwald & Prinstein, Reference Brechwald and Prinstein 2011 ), including media preferences (Valkenburg & Cantor, Reference Valkenburg, Cantor, Zillmann and Vorderer 2000 ). Members of a peer group share norms that they have created themselves. Adolescents typically form strong social antennas for these norms, including those pertaining to social media use. Environmental influences on social media use can thus occur overtly (e.g., by parental restriction or monitoring) or more covertly, for example through adolescents’ sensitivity to the prevailing norms in their peer group .

The Conditionality Paradigm

The conditionality paradigm is closely linked with the selectivity and transactionality paradigms. After all, in both paradigms it is argued that only the messages that individuals select in response to person-specific antecedents have the potential to influence them. Theories that propose conditional media effects share the notion that media effects (a) do not equally hold for all media users, and (b) can be enhanced or reduced by dispositional, developmental, and social-context factors (Valkenburg & Peter, Reference Valkenburg and Peter 2013a ). In line with earlier media effects theories (e.g., Bandura, Reference Bandura, Bryant and Oliver 2009 ), Valkenburg and Peter’s DSMM postulates that dispositional, developmental, and social-context factors may have a double role in the media effects process: They not only predict media use, but they also influence the way in which media messages are processed and subsequent distal media outcomes. This twofold influence results in three types of differential susceptibility to media effects: dispositional, developmental, and social-context susceptibility.

Dispositional Susceptibility

Dispositional susceptibility refers to the degree to which certain dispositions influence media processing and media outcomes. It has been shown, for example, that trait aggressiveness can increase the effects of media violence on cognitive and emotional processing of violent media content (Schultz et al., Reference Schultz, Izard and Bear 2004 ), which may, in turn, result in enhanced aggression (Krcmar, Reference Krcmar, Nabi and Oliver 2009 ). As for social media, it has been shown that Facebook users who scored high on FOMO, experience more hurtful comments, and more stalking and harassment (Buglass et al., Reference Buglass, Binder, Betts and Underwood 2017 ). In addition, sensation seeking is an important predictor of risky behavior on social media, whereas a lack of inhibitory control can result in more negative feedback on these media (Koutamanis et al., Reference Koutamanis, Vossen and Valkenburg 2015 ). Finally, specific affordances of social media may particularly stimulate online disinhibition among self-conscious and socially anxious adolescents (e.g., Schouten et al., Reference Schouten, Valkenburg and Peter 2007 ). This online disinhibition has been shown to result in positive (e.g., friendship closeness; Valkenburg & Peter, Reference Valkenburg and Peter 2009 ) or negative effects of social media use (e.g., cyberbullying; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein 2018b ).

Developmental Susceptibility

Developmental susceptibility refers to the degree to which developmental level influences media processing and media outcomes. Evidence for developmental susceptibility is relatively scarce. It has been shown that younger children react with stronger physiological arousal to violent and frightening audiovisual content than adolescents, even if this content is unrealistic, which may enhance the effects of such content (Cantor, Reference Cantor, Bryant and Zillmann 2009 ). In addition, online sexual risk behavior seems to reach a peak in middle adolescence, after which it levels off again (Baumgartner et al., Reference Baumgartner, Sumter, Peter and Valkenburg 2012 ). This developmentally induced inverted U-shaped trajectory is often explained by dual-system theories of brain development (e.g., Steinberg, Reference Steinberg 2010 ), which argue that the parts of the adolescent brain that are responsible for reward sensitivity to social stimuli may develop more quickly than the parts that are responsible for regulation of this reward sensitivity.

Social-Context Susceptibility

Social-context susceptibility refers to the degree to which social context factors influence media processing and media outcomes. Evidence for social-context susceptibility comes from studies showing that when physical violence is normative in families, children may learn to interpret media violence differently (Schultz et al., Reference Schultz, Izard and Bear 2004 ), making them more susceptible to media effects on aggression (Fikkers et al., Reference Fikkers, Piotrowski, Weeda, Vossen and Valkenburg 2013 ). Social-context susceptibility can be explained by the context-convergence hypothesis (Valkenburg & Peter, Reference Valkenburg and Peter 2013a ), which posits that individuals are more susceptible to media messages if these messages converge with the values and norms in their social context. In cultivation theory (Gerbner et al., Reference Gerbner, Gross, Morgan and Signorielli 1980 , p. 15), an early media effects theory, this phenomenon has been named resonance: When something experienced in the media is similar to the norms that prevail in one’s social environment, it creates a “double dose” of the message, which enhances the likelihood of media effects.

Social Media as a Social Context in Its Own Right

As discussed earlier on in the chapter, social context refers to the environment within which individuals or groups act or interact, and whose norms and affordances may influence the cognitions, emotions, attitudes, and behaviors that occur within it. An important theoretical question is whether we need to conceptualize social media as a social context in its own right that may shape both social media uses and their effects. Authors differ in their conceptions of whether social media should be seen as a social context in itself. Some scholars adhere to a “Mirroring Framework” (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein 2018a , p. 268), that is, the notion that adolescents’ experiences on social media simply mirror their offline experiences.

Several other scholars, including the author of this chapter, believe that social media is not merely a technology, but a social context, whose norms and affordances may influence social media use, as well as the changes among users that result from this use. These scholars do acknowledge that the social media context overlaps with other contexts, such as the family, peer, and school context. But such overlap also applies to other social contexts (e.g., family with school; peer group with school). Coconstruction theory (Subrahmanyam et al., Reference Subrahmanyam, Smahel and Greenfield 2006 ) and the transformation framework (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein 2018a , Reference Nesi, Choukas-Bradley and Prinstein 2018b ) both discuss how the social media context differs from equivalent offline interaction contexts. Coconstruction theory proposes that even though adolescents construct the same developmental issues online as they do offline, they use specific affordances of social media that do not exist in offline situations (e.g., cue manageability and scalability) to construct and coconstruct their identity, intimacy, and sexuality. Finally, following affordance theories of social media (e.g., boyd, Reference boyd and Papacharissi 2011 ; McFarland & Ployhart, Reference McFarland and Ployhart 2015 ; Peter & Valkenburg, Reference Peter, Valkenburg and Scharrer 2013 ), the transformation framework considers social media as a context that differs in important ways from face-to-face and earlier digital interactions (e.g., email). As a result, this context may affect social media uses and their effects in different ways than face-to-face and earlier digital interactions (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein 2018a , Reference Nesi, Choukas-Bradley and Prinstein 2018b ).

A telling example of a defining norm of the social media context is its positivity bias, which refers to the observation that public social media interactions (e.g., Instagram, Facebook) are typically more positive than equivalent offline interactions (e.g., Reinecke & Trepte, Reference Reinecke and Trepte 2014 ; Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg 2017 ). This positivity bias may influence both message recipients and message senders positively or negatively. Message recipients can be exposed to positively biased messages of happy, successful, and popular peers. Among some recipients this exposure may result in envy and negative psychosocial effects (e.g., Vogel et al., Reference Vogel, Rose, Roberts and Eckles 2014 ). And among other recipients it may lead to inspiration, and positive psychosocial effects (e.g., Meier et al., Reference Meier, Gilbert, Börner and Possler 2020 ).

The positivity bias may also influence message senders in opposite ways. Firstly, their positively biased self-presentations may increase their own psychological well-being (Burnell et al., Reference Burnell, George and Underwood 2020 ), a phenomenon that has been named a self-effect (Valkenburg, Reference Valkenburg 2017 ). But when these self-presentations are exaggerated (e.g., too emotional) they may create embarrassment and guilt, and decrease psychological well-being (Stern, Reference Stern 2015 ). Apparently, the perceptions and consequences of the positivity bias on social media differ from adolescent to adolescent, an idea that will be elaborated upon when discussing affordance theories of social media .

Computer-Mediated Communication Theories

Studies into the cognitive, affective, and behavioral effects of social media have often been inspired by theories of computer-mediated communication (CMC). CMC theories and research emerged in the 1970s, long before the Internet became widespread. Unlike media effects research, which evolved from the study of mass communication, CMC research originated from a mixture of interpersonal communication, teleconferencing, and organizational behavior. In addition, whereas media effects research is more survey-oriented, the approach of CMC research is mostly experimental. CMC research has typically focused on comparing the cognitive, affective, and behavioral effects of face-to-face communication to those of CMC. It has often centered on questions such as whether and how certain CMC properties, such as anonymity or the lack of audiovisual cues, influence the quality of social interaction among dyads or group members, and the impressions these dyads or group members form of one another.

In the 1970s, some early, rather pessimistic CMC theories compared the “lean” text-only CMC with the “rich” communication in face-to-face settings. In doing so, they tried to explain, for example, why CMC leads to less intimacy and more disinhibited behavior (Walther, Reference Walther, Knapp and Daly 2011 ). In the early 1990s, a new cluster of theories emerged, with a more optimistic view on CMC. That was the time that individuals started emailing, and the Internet became available for personal use. During this time, Walther’s social information processing theory became influential. This theory explains how CMC partners can gradually overcome the presumed limitations of CMC by creatively employing strategies to exchange and understand social and emotional messages in CMC. In this way, with sufficient time and message exchanges, CMC partners could develop intimacy levels comparable to those in face-to-face communication (Walther, Reference Walther 1992 ).

In the second half of the 1990s, Walther extended his theory with an even more optimistic perspective, which predicted that CMC messages could lead to greater intimacy than face-to-face communication. According to his hyperpersonal communication model (Walther, Reference Walther 1996 ), the relative anonymity and reduced audiovisual cues in CMC encourage individuals to optimally present themselves, for instance, by pretending to be kinder and more beautiful than they actually are. Meanwhile, the recipients of these optimized self-presentations are free to fill in the blanks in their impressions of their partners, which may encourage them to idealize these partners. In doing so, CMC relationships could become “hyperpersonal,” that is, more intimate than offline relationships (Walther, Reference Walther 1996 ). In the same period, another influential CMC theory emerged, the social identity model of deindividuation effects, whose major focus was to explain how the anonymity in CMC groups affects normative and anti-normative behavior among their members (Postmes et al., Reference Postmes, Lea, Spears and Reicher 2000 ).

The focus of early CMC theories on anonymity and limited audiovisual cues fitted well in the 1990s and the first half of the 2000s, when CMC was predominantly text-based and typically took place in anonymous chatrooms or newsgroups (Valkenburg et al., Reference Valkenburg, Peter and Walther 2016 ). However, most current CMC technologies popular among adolescents, such as Instagram and Snapchat, are much less anonymous than their predecessors, and rely heavily on a range of audiovisual cues. Therefore, it has become less relevant to experimentally compare their specific CMC properties with face-to-face communication (Scott & Fullwood, Reference Scott and Fullwood 2020 ). Moreover, the “computer” part of CMC applications has become more portable and ubiquitous, and has diluted into a multitude of mobile devices and apps (Xu & Liao, Reference Xu and Liao 2020 , p. 32). Indeed, the devices with which we communicate have gotten closer and closer to our bodies. They moved from our desks (desktop), to our bags (laptop), to our pockets (smartphone), and to our wrists (Valkenburg & Piotrowski, Reference Valkenburg and Piotrowski 2017 ). It is no surprise that these rapid developments provide contemporary CMC theorists with many new conceptual, theoretical, and empirical challenges (Carr, Reference Carr 2020 ).

An important strength of CMC theories and research, certainly when compared with media effects theories, has been their strong focus on the dynamic give-and-take interactions between message senders and recipients. CMC theories are, by definition, transactional theories that acknowledge that message exchanges are shaped by both message senders and receivers (Valkenburg, Reference Valkenburg 2017 ). However, possibly due to its experimental orientation, CMC research has often focused on the unidirectional, across-the-board effects of CMC properties (i.e., anonymity, reduced audiovisual cues) on the recipients of these properties. Although both media effects and CMC theories like to describe recipients as active in the sense that they have autonomy over the way they interpret media or CMC characteristics, the empirically investigated influence in both research traditions is still all too often unidirectional: from the media or technology to the recipients.

However, if we accept that the current generation of social media are not merely technologies, but a social context whose norms and affordances differ from offline social contexts, such as the peer group or the neighborhood (Sameroff, Reference Sameroff and Sameroff 2009 ), we may need an updated theorization on the uses and effects of social media. Such an update needs to address the transactional relationships between social media users and the social media context, as well as the interactions between the social media context and other, offline, contexts. In the next section, I will make a preliminary start on such an update, by introducing a transactional affordance theory of social media uses. I deliberately use the term “uses” to refer to the many possible uses of social media.

Three types of theories might offer inspiration to such an updated theorization: transactional theories of development (e.g., Bronfenbrenner, Reference Bronfenbrenner and Bronfenbrenner 2005 ; Sameroff, Reference Sameroff and Sameroff 2009 ), Gibson’s ( Reference Gibson 1979 ) affordance theory, which later evolved into affordance theories of social media (e.g., boyd, Reference boyd and Papacharissi 2011 ; Treem & Leonardi, Reference Treem and Leonardi 2013 ), and self-effects theory (Valkenburg, Reference Valkenburg 2017 ). Transactional theories of development propose that change within an adolescent is a product of their continuous dynamic interactions with their experienced social contexts (Bronfenbrenner, Reference Bronfenbrenner and Bronfenbrenner 2005 ; Sameroff, Reference Sameroff and Sameroff 2009 ). Gibson’s affordance theory is a learning theory that explains how different perceptions of an object or environment can result in different actions toward or uses of this object or environment. Finally, self-effects are the effects of messages on message senders themselves. As will be clear, social media use cannot only result in transactions (i.e., changes) within message recipients , but also within the senders of these messages .

A Transactional Affordance Theory of Social Media Uses

A transactional affordance theory of social media uses elaborates on three related propositions raised in transactional theories and/or affordance theories and/or self-effects theory: These propositions are: (1) social media users (co)create their own social media context, and this (co)created context shapes their experienced effects; (2) just like the family, school, and peer context, the social media context is a micro-level social context, in which transactional effects are more likely than in the mass media context; (3) the experiences with the social media context differ from adolescent to adolescent; thus, the unique way in which an adolescent experiences the norms, affordances, and messages in this context is the driving force of social media effects on this adolescent.

Social Media Users Shape Their Own Effects

The first proposition is that (1) social media users can individually (or collectively) shape their social media context, and (2) their experiences within this social media context can shape the effects of this context. The first part of this proposition is in line with transactional theories of development and Gibson’s ( Reference Gibson 1979 ) affordance theory. Transactional theories of development agree that children can shape and be shaped by their experienced social contexts (Bronfenbrenner, Reference Bronfenbrenner and Bronfenbrenner 2005 ; Sameroff, Reference Sameroff and Sameroff 2009 ). Likewise, Gibson argued that individuals tend to alter their environment by adjusting its affordances to better suit their needs and desires. In other words, an individual’s perceptions of the affordances of a context may lead to specific uses of this context, which in turn shape the experienced effects of this context. A similar proposition has been raised in self-effects theory (Valkenburg, Reference Valkenburg 2017 ), which proposes that social media users carefully craft their messages (e.g., social media posts), which may influence the recipients of these messages (i.e., the social environment) but also the message senders themselves, directly via internalization of overt behavior (Bem, Reference Bem and Berkowitz 1972 ), or indirectly, via the feedback that their messages elicit.

The first part of this proposition, that social media users can individually (or collectively) shape their social media context, has received support. Adolescents can (co)create both the affordances and norms of the social media contexts in which they participate. It has been found, for example, that the sharing of intimate, self-related information is more accepted in the social media context than in equivalent offline contexts (Christofides et al., Reference Christofides, Muise and Desmarais 2009 ). Another (co)created norm is that the sharing of negative emotions is more accepted in private (e.g., WhatsApp) than public social media contexts (e.g., Instagram; Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg 2017 ). And if adolescents do want to share intimate, self-related information on a public social medium like Instagram, they sometimes turn to a Finsta (a Fake Instagram account where they can be honest and show their true self) in addition to a Rinsta (a Real Instagram account used to post their positive experiences). Finally, overly emotional expressions on in public social media are considered norm violations (Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg 2017 ).

The second part of this proposition, that adolescents’ unique experiences within their (co) created social media context can shape the effects of this context, has also received support. For example, message recipients can selectively and autonomously expose themselves to uplifting or depressing social media messages, which may subsequently affect their well-being in unique ways. In a qualitative study of Rideout and Fox ( Reference Rideout and Fox 2018 ), one adolescent reported: “If I’m feeling depressed, getting on Twitter and seeing funny tweets or watching funny videos on YouTube can really brighten my mood” (p. 20). In this example, a transient dispositional variable (low mood) shaped this adolescent’s selective exposure, which in turn positively shaped their experienced effect (i.e., a brightened mood). In the same study, another adolescent’s preexisting low mood resulted in an opposite effect of social media browsing (i.e., a worsened low mood): “Social media makes me feel worse when I’m scrolling through feeds and seeing news headlines and posts about how terrible something is” (Rideout & Fox, Reference Rideout and Fox 2018 , p. 19). And yet another adolescent with a preexisting low mood reacted with selective avoidance: “Usually friends post happy things – getting together with others, accomplishments, bragging. I don’t always want to see it when I’m feeling down about myself so I stay off social media” (p. 20).

These qualitative finding illustrate the complex nature of the associations between preexisting disposition (i.e., low mood), selective exposure to social media messages, and postexposure mood. Mood-induced selective exposure to social media messages can enhance mood (adolescent 1), worsen mood (adolescent 2), and it can lead to selective avoidance (adolescent 3). Such unique differences have also been reported in two recent experience sampling studies by Beyens et al. ( Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg 2020 , Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg 2021 ), who found considerable differences in experienced effects of social media use. In one study, they found that 46% of the participating adolescents felt better after social media browsing in the past hour, while 44% did not feel better or worse, and 10% felt worse after such use (Beyens et al., Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg 2020 ).

Such uniquely experienced social media effects also seem to hold for message senders. Several studies have shown that message sending (e.g., posting) can improve the well-being of message senders (Verduyn et al., Reference Verduyn, Ybarra, Résibois, Jonides and Kross 2017 ), a result that has often been explained by the positive feedback that message senders receive (Verduyn et al., Reference Verduyn, Ybarra, Résibois, Jonides and Kross 2017 ). However, social media–induced improvements in well-being can also occur without any involvement of fellow users (Pingree, Reference Pingree 2007 ; Valkenburg, Reference Valkenburg 2017 ). Self-expressions on social media, especially when their intended audience is sizeable, may lead to internalization of these self-expressions, for example, via self-perception. Self-perception theory (Bem, Reference Bem and Berkowitz 1972 ) argues that individuals infer their internal self-concept from retrospectively observing their own overt behavior. If these individuals share positive self-expressions induced by the positivity norm in public social media, these individuals may, due to a desire for a consistency between their overt behavior and their self-concept, adjust their self-concept to match their behavior. For a discussion of self-effects in social media, and the mechanisms that may explain such effects, such as cognitive reframing, biased scanning, and public commitment, see Valkenburg ( Reference Valkenburg 2017 ).

Social Media as a Micro- and Mesosystem

A second proposition of a transactional affordance theory of social media uses is that the social media context is a micro-level context, in which effects on participants are more likely than in the mass media context. Bronfenbrenner was one of the first to conceptualize the relationship between individuals and their social contexts. He distinguished between four types of contexts: the micro-, meso-, macro-, and exosystem (Bronfenbrenner, Reference Bronfenbrenner 1979 , Reference Bronfenbrenner and Bronfenbrenner 2005 ). The microsystem involves direct interactions of the child with their most proximal circle, such as the family, peer group, or neighborhood. The mesosystem represents the possible interactions among these microsystems (e.g., between the family and peer group), whereas the macrosystem refers to the overarching culture or subculture of children. Bronfenbrenner’s fourth context, the exosystem, refers to social contexts that do not allow the child as an active participant but that have the potential to affect the child. An example of an exosystem is the work context of one of the parents of the child. A child cannot actively participate in this context but can in many ways be influenced by it.

At the time of the development of his theory, Bronfenbrenner identified the mass media as an exosystem because it did not allow for active involvement of adolescents, even though it could shape their experiences. Although valid at the time, Bronfenbrenner (1917–2005) could not have foreseen the rapid developments within the media landscape. If he could have, he would probably have categorized the social media context as a microsystem rather than an exosystem. After all, unlike before, the media landscape now does allow for, and even stimulates, direct interactions among participants. For example, idols, an important source of identity formation in adolescence, have been transferred from the exosystem to the microsystem: Whereas movie stars or pop singers used to be celebrities that adolescents could admire from an unsurmountable distance, social media now provide them with ample opportunity for direct communication with their idols. In fact, many of their contemporary idols are YouTubers or Instagram influencers with whom they can directly interact.

If Bronfenbrenner could, he may now also have identified the social media context as part of the mesosystem because it allows for, or even stimulates, interactions with other microsystems (e.g., the family or the peer contexts). Although every traditional microsystem is in part “permeable” to the influences from other microsystems (e.g., family to peers and vice versa; family to school and vice versa), the social media context might be much more permeable to such influences. Conversely, the social media context seems to have penetrated all other microsystems in which adolescents participate, ranging from the family and peer context to the school.

However, if we accept the social media context as a microsystem, we must acknowledge that this context may, due to its proximity, dynamic, and ubiquitous nature, enhance the likelihood of effects on its participants, certainly when compared to the traditional mass media context. And if we accept the social media context as a part of the mesosystem (interactions among microsystems), we need to acknowledge that it may interact with the norms and affordances of other microsystems, such as parents or the school. And such interactions do occur. For example, preventing or counteracting possible negative consequences of social media interactions, and explaining to adolescents that the social media context may not be as perfect as it often appears, are important ingredients of today’s media-specific parenting and school-based prevention and intervention programs (Valkenburg & Piotrowski, Reference Valkenburg and Piotrowski 2017 ).

It Is the Subjective Experience That Counts

A third and final proposition of a transactional affordance theory of social media uses is that the unique way in which individuals experience the norms and affordances of the social media context is the driving force of transactional effects between individuals and this context. This proposition is consistent with both transactional theories of development (Bronfenbrenner, Reference Bronfenbrenner and Bronfenbrenner 2005 ; Sameroff, Reference Sameroff and Sameroff 2009 ) and Gibson’s affordance theory (Gibson, Reference Gibson 1979 ). Affordances, according to Gibson, are the unique ways in which individuals experience the utility of objects. For example, distinct individuals may all perceive another utility of a bottle (e.g., as a water container, a vase, a candle holder, or a weapon). However, to understand such individual differences in experiences of the affordances of social media, I first specify some of these affordances and argue how and why these affordances differ from other micro-level social contexts, such as the family or peer contexts.

A growing number of social media scholars have ventured to identify specific affordances of social media (boyd, Reference boyd and Papacharissi 2011 ; McFarland & Ployhart, Reference McFarland and Ployhart 2015 ; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein 2018a , Reference Nesi, Choukas-Bradley and Prinstein 2018b ; Sundar et al., Reference Sundar, Jia, Waddell, Huang and Sundar 2015 ; Treem & Leonardi, Reference Treem and Leonardi 2013 ; Valkenburg & Peter, Reference Valkenburg and Peter 2011 ; Valkenburg & Piotrowski, Reference Valkenburg and Piotrowski 2017 ). Some of these scholars have identified four affordances (Treem & Leonardi, Reference Treem and Leonardi 2013 ), others have focused on seven (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein 2018a ; Valkenburg & Piotrowski, Reference Valkenburg and Piotrowski 2017 ) or even eight affordances (McFarland & Ployhart, Reference McFarland and Ployhart 2015 ). Many comparable affordances appear in different studies but sometimes under different names (e.g., identifiability vs. cue absence; scalability vs. publicness). In this chapter, the focus is on three affordances that have been mostly identified in earlier literature. For each affordance, I discuss the scarce evidence of individual differences in the perceptions of its utility, as well as its potential consequences for both senders and recipients of social media messages. A more elaborate discussion of these consequences can be found in Nesi et al. ( Reference Nesi, Choukas-Bradley and Prinstein 2018a , Reference Nesi, Choukas-Bradley and Prinstein 2018b )

Asynchronicity

Most social media are asynchronous, that is, they afford their users the possibility to edit and reflect on their messages and pictures before uploading them. Even in more synchronous apps, such as WhatsApp, users must press the send button before they can transmit their message or photo to partners or group members. Asynchronous communication allows message senders to carefully craft, refine, and optimize their self-presentations. Adolescents differ significantly in the importance they attach to this affordance. In one of our survey studies, we asked (pre)adolescents (10–17-year-olds) how much importance they attached to the idea that they have more time to think about what they share on social media than in face-to-face encounters (this part of data not published). Thirty-seven percent of them attached importance or high importance to this affordance, 25% did not attach any importance to this affordance, and a remaining 38% reported that they did not care. The asynchronicity affordance seemed particularly valuable for early and middle adolescents (12–15-years-olds), socially anxious, and lonely adolescents, who apparently benefit most from the extra time to optimize their self-presentations (Peter & Valkenburg, Reference Peter and Valkenburg 2006 ).

The asynchronicity affordance may influence both senders and recipients of social media messages. The optimized self-presentations of senders could lead to self-effects through internalization of these self-presentations (Valkenburg, Reference Valkenburg 2017 ). Such optimized self-presentations can also influence message recipients in both positive and negative ways. They can evoke empathy, laughter, or a positive mood, but in case they are optimized to hurt recipients, they can also lead to painful experiences among recipients (Rideout & Fox, Reference Rideout and Fox 2018 ; Valkenburg & Peter, Reference Valkenburg and Peter 2013a ) .

Cue Manageability

Most social media offer their users the possibility to show or hide visual or auditory cues about the self. Social media users can decide whether they present themselves only through textual descriptions or whether they add more cues, such as pictures or video clips. Moreover, by means of specific software, they can edit, manipulate, and optimize these cues. Adolescents differ greatly in the importance they attach to the cue-manageability affordance. For example, in one of our studies, 8% of adolescents deemed it important or very important that others cannot see them while communicating on social media, whereas 55% deemed it as unimportant, and 37% reported that they did not care (this part of the data not published). The cue-manageability affordance seems particularly valuable for female adolescents, socially anxious adolescents, and adolescents high in private self-consciousness (e.g., I am generally attentive to my inner feelings), and public self-consciousness (e.g., I usually worry about making a good impression; Schouten et al., Reference Schouten, Valkenburg and Peter 2007 ).

Like the asynchronicity affordance, cue management affords adolescents possibilities to optimize their online self-presentations, which can lead to positive self-effects, for example via self-perception (Bem, Reference Bem and Berkowitz 1972 ) or to cognitive reframing (an intra-individual change in how previous experiences are viewed). However, when the self-presentations are exaggerated (e.g., too intimate or childish), they can violate the norms of the social media context, and they may trap adolescents in uncomfortable situations, in which they may become ridiculed or socially rejected (Peter & Valkenburg, Reference Peter, Valkenburg and Scharrer 2013 ).

Scalability

Scalability offers social media participants the ability to articulate self-related messages and photos to any size and nature of audiences. It thus provides message senders with ample forums to commit themselves to realistic or imagined social media audiences. This may be preeminently attractive to adolescents, whose egocentrism (i.e., their inability to distinguish between their perception of what others think and what others actually think of them) may result in their perception of an imaginary audience that is constantly observing their actions (Elkind, Reference Elkind 1967 ).

To my knowledge, no research has demonstrated individual differences in the value attached to the scalability affordance, and this may, therefore, be an interesting question for future research. The scalability affordance may enhance self-effects through public commitment. When individuals believe that their self-presentations are public, the likelihood of internalization enhances (Kelly & Rodriguez, Reference Kelly and Rodriguez 2006 ), not only because other people can see their presentations, but also because individuals do not like to appear inconsistent in their public self-presentations (Tice, Reference Tice 1992 ).

The three affordances of social media are all important in their own right but they have an important overarching affordance in common: They offer social media users greater controllability of their self-presentations than face-to-face interactions or older technologies do (Valkenburg & Peter, Reference Valkenburg and Peter 2011 ). This controllability means that social media users can choose not only what, but also how, when, and to whom in the global village they can present themselves. This controllability may offer social media users a sense (or an illusion) of security, which makes some of them feel freer in their interpersonal interactions than they can experience in other micro-level social contexts. This sense (or illusion) of security and freedom is particularly important for adolescents, who typically experience enhanced uncertainty about their identity (i.e., how to define who they are and will become), intimacy (i.e., how to form and maintain meaningful relationships), and sexuality (e.g., how to cope with sexual desire and define their sexual orientation; Steinberg, Reference Steinberg 2011 ). This enhanced controllability of self-presentations may, therefore, be a major explanation of adolescents’ attraction to social media (Valkenburg & Peter, Reference Valkenburg and Peter 2011 ) .

Conclusions and Avenues for Future Research

In this chapter, I conceptualized social media as a social context in its own right, and borrowing from Bronfenbrenner’s ( Reference Bronfenbrenner 1979 ) typology, as a social context that frequently interacts with other micro-level contexts, such as the family, peer group, and school. I also explained how the social media context differs from the traditional mass media context and why it can lead to stronger effects on both message senders and recipients. The social media context is not only more proximal and ubiquitous than the mass media context, but it is also more dynamic in the sense that everyone can actively participate in and contribute to it. Whereas the “effects” of mass media have mostly been conceptualized as recipient effects in earlier research, social media inherently point our attention to self-effects: the messages produced by the sender on themself. The emphasis on self-effects is important for future social media research because it implies a focus on theories accounting for intra-individual transactions as a result of one’s own affordance-induced behavior, next to theories explaining intra-individual transactions among recipients that occur as a result of selective attention and perception of messages sent by others.

Consistent with Gibson’s ( Reference Gibson 1979 ) affordance theory, this chapter revealed that adolescents differ greatly in their perceptions of some of the affordances of social media. Preliminary work also suggest that they also differ greatly in the effects they experience in the social media context (Pouwels et al., Reference Pouwels, Valkenburg, Beyens, van Driel and Keijsers 2021 ; Valkenburg et al., Reference Valkenburg, Beyens, Pouwels, van Driel and Keijsers 2021 ). Unfortunately, social media effects research still all too often focuses on universal effects. This may in part be due to the experimental focus of the CMC research tradition, in which individual differences are typically disregarded, because they are assumed to be canceled out by random assignment (Bolger et al., Reference Bolger, Zee, Rossignac-Milon and Hassin 2019 ). If such individual differences are measured at all, they are often included as covariates rather than as factors that may interact with the experimental condition (Valkenburg & Peter, Reference Valkenburg and Peter 2013b ).

There is a need for future research focusing on transactional and person-specific effects of social media use. Qualitative studies have repeatedly demonstrated that adolescents can differ substantially in their media use, their experiences on social media, and the effects of social media use (e.g., Rideout & Fox, Reference Rideout and Fox 2018 ). However, most quantitative studies into the psychosocial effects of social media still adopt a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators, such as gender or age (Beyens et al., Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg 2020 ; Howard & Hoffman, Reference Howard and Hoffman 2017 ). However, due to technological advancements, it has become feasible to collect masses of intensive longitudinal data from masses of individuals on the uses and effects of social media (e.g., through experience sampling or tracking). Moreover, rapid developments in data mining and statistical methods now also enable researchers to analyze highly complex N = 1 time series data, and by doing so, to develop and investigate media effects and other communication theories bottom up (i.e., from the individual adolescent to the population or subpopulation) rather than top down (i.e., from the population to the adolescent; Lerner et al., Reference Lerner, Lerner and Chase 2019 ).

In our recent and current experience sampling studies, we have adopted such a person-specific, N = 1 time series approach (McNeish & Hamaker, Reference McNeish and Hamaker 2020 ). Up to now, our results show striking differences in adolescents’ susceptibility to the momentary effects of social media on well-being (Beyens et al., Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg 2020 ), self-esteem (Valkenburg et al., Reference Valkenburg, Beyens, Pouwels, van Driel and Keijsers 2021 ), and friendship closeness (Pouwels et al., Reference Pouwels, Valkenburg, Beyens, van Driel and Keijsers 2021 ). In all these studies, the effect sizes of social media use on outcomes ranged from moderately or strongly negative to moderately or strongly positive. For example, the within-person effect sizes of social media browsing on well-being ranged from β = −0.24 to β = +0.68 across adolescents. Likewise, the effects of Instagram use on friendship closeness ranged from β = −0.57 to β = +0.45. And the effects of social media use on self-esteem led to lagged effect sizes ranging from β = −0.21 to β = +0.17.

Unfortunately, we still do not know how these short-term effects of social media use accumulate into longer-term effects, and this is an important avenue for future research. Moreover, up to now we do not know whether the person-specific effects that we found can be attributed to (stable or transient) dispositional, developmental, and/or (situational or structural) social-context factors. An important avenue for future research is to explain why social media use can lead to “positive susceptibles” (i.e., adolescents who mainly experience positive effects of social media use), “negative susceptibles” (adolescents who mainly experience negative effects of social media use, and “nonsusceptibles” (adolescent who are predominantly unaffected by social media use). After all, only if we know which, when, how, and why adolescents may be influenced by certain types of social media use will we be able to adequately target prevention and intervention strategies to these adolescents .

The first part of this chapter is largely based on Valkenburg, Peter, and Walther (2016), Media effects: Theory and research. Annual Review of Psychology , 67 , 315–338.

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

Theoretical Foundations of Social Media Uses and Effects
By Patti M. Valkenburg
Edited by Jacqueline Nesi , Brown University, Rhode Island , Eva H. Telzer , University of North Carolina, Chapel Hill , Mitchell J. Prinstein , University of North Carolina, Chapel Hill
Book: Handbook of Adolescent Digital Media Use and Mental Health
Online publication: 30 June 2022
Chapter DOI: https://doi.org/10.1017/9781108976237.004

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Antecedents of social media addiction in high and low relational mobility societies: Motivation to expand social network and fear of reputational damage

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Affiliation Graduate School of Human Sciences, Osaka University, Osaka, Japan

Shuma Iwatani,
Eiichiro Watamura

Published: April 18, 2024
https://doi.org/10.1371/journal.pone.0300681
Reader Comments

Contrary to previous studies on the antecedent factors of social media addiction, we focused on the social environmental factor of relational mobility (i.e., the ease of constructing new interpersonal relationships) and investigated its relationship with social media addiction. People in low relational mobility societies have fewer opportunities to select new relationship partners and consequently feel a stronger need to maintain their reputation. We hypothesized that (1) people in low relational mobility societies are more strongly addicted to social media because they estimate that greater reputational damage will be caused by ignoring messages and (2) people in low relational mobility societies estimate greater reputational damage than actual damage. We conducted two online experiments with 715 and 1,826 participants. Our results demonstrated that (1) there is no relationship between relational mobility and social media addiction and (2) people in both high and low relational mobility societies overestimate reputational damage. Furthermore, we demonstrated that the social media addiction mechanism differs between societies: (3) people in low relational mobility societies estimate greater reputational damage, whereas (4) people in high relational mobility societies are more motivated to expand their social networks; both mechanisms strengthen their social media addiction. Based on these results, we propose interventions for moderating social media addiction in both high and low relational mobility societies.

Citation: Iwatani S, Watamura E (2024) Antecedents of social media addiction in high and low relational mobility societies: Motivation to expand social network and fear of reputational damage. PLoS ONE 19(4): e0300681. https://doi.org/10.1371/journal.pone.0300681

Editor: Giulia Ballarotto, University of Rome La Sapienza: Universita degli Studi di Roma La Sapienza, ITALY

Received: August 9, 2023; Accepted: March 1, 2024; Published: April 18, 2024

Copyright: © 2024 Iwatani, Watamura. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and R code are publicly available via the Open Science Framework and can be accessed at https://osf.io/ch5py/

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Social media platforms have both positive and negative effects on people [ 1 ]. On the positive side, it allows people to obtain information, send messages from anywhere, and communicate more closely with others. On the negative side, some users are addicted to social media and spend excessive time on it. In this study, we attempted to demonstrate the mechanism through which people become addicted to social media.

The term "addiction" has various meanings, and its definition has expanded over the years. It can be classified into two main categories: substance addiction and non-substance addiction [ 2 ] (non-substance addiction is also referred to as behavioral addiction [ 3 ]). Substance addiction is a neuropsychiatric disorder characterized by a recurring desire to ingest substances, such as drugs or alcohol, despite harmful consequences [ 2 , 4 ]. People who require daily intake of alcohol are defined as being addicted to alcohol. By contrast, non-substance addiction refers to addiction to things other than substances [ 5 – 7 ], such as pathological gambling, the Internet, and mobile phones [ 2 ].

Social media addiction is a type of non-substance addiction. The following are some definitions of social media addiction: “irrational and excessive use of social media to the extent that it interferes with other aspects of daily life” [ 5 ], “excessive use and habitual monitoring of social media, manifested in compulsive usage that comes at the expense of other activities” [8, p.747], and “being overly concerned about SNSs, to be driven by a strong motivation to log on to or use SNSs, and to devote so much time and effort to SNSs that it impairs other social activities, studies/job, interpersonal relationships, and/or psychological health and well-being” ([ 9 ], p.4054; SNS: social network service). The common items in these definitions are (i) devoting excessive time to social media and (ii) the negative consequences of using social media (i.e., interfering with other social activities such as studying, job, interpersonal relationships, psychological health, and well-being).

Social media addiction has various negative effects, including damaging mental health [ 5 ], poor life satisfaction [ 10 ], and chronic physical issues, such as neck pain or headaches [ 11 ]. Some studies that focused on company employees have demonstrated that social media addiction leads to a reduction in sleeping hours [ 12 ], increased distraction at workplace [ 12 ], and impaired productivity [ 8 ].

In this study, we used the term “addiction” or “social media addiction” in a non-clinical sense. This is because social media addiction is not included in the DSM-5-TR classification [ 13 ] and no study has demonstrated that social media addiction can have severe physical consequences [ 14 ].

1.1. Antecedents of social media addiction

Previous studies have investigated various antecedents of social media addiction such as neuroticism [ 15 ], lack of self-control [ 16 ], and extraversion [ 17 ], and have several perspectives on social media addiction [ 18 ]. One perspective focuses on dispositional differences such as attachment styles. D’Arienzo et al. [ 19 ] concluded that avoidant or insecure attachment style is associated with stronger social media addiction. Additionally, an empirical study by Ballarotto et al. [ 20 ] demonstrated that individuals who are less attached to their parents are more strongly addicted to Instagram. Eroglu [ 21 ] showed that people with insecure attachments (i.e., those having negative “internally working models about both themselves and others” [ 21 ] p.151) are more strongly addicted to Facebook. Additionally, Monacis et al. [ 22 ] demonstrated that people with avoidant attachment style (i.e., those who experience discomfort with intimacy) are more strongly addicted to social media.

Furthermore, some studies have focused on the motivation to use social media. For example, those who feel lonely are more strongly addicted to social media [ 23 ] as they are motivated to connect with others [ 24 ]. Moreover, extraverts are more strongly addicted to social media [ 17 ] as they use it to expand their social connections [ 25 ]. Additionally, those with a higher motivation to expand their social network would be more strongly addicted to social media, as it allows them to maintain or expand their social network.

Additionally, demographic variables, such as sex and age, may be related to social media addiction Mari et al. [ 26 ] found that females are more strongly addicted to the Internet, whereas Su et al. [ 27 ] and Alnjadat et al. [ 28 ] found that males are more strongly addicted to the Internet or social media. Moreover, age is related to social media addiction as younger individuals are more strongly addicted to social media [ 25 ].

Also, distressing changes in social situations, such as those during and after the COVID-19 pandemic, may also strengthen social media addiction. Recent studies have noted that the importance of social media as a medium for rapid information dissemination has increased after COVID-19 [ 29 ] and demonstrated that psychological distress owing to COVID-19 has strengthened social media [ 30 ], Internet [ 20 ], and Instagram [ 20 ] addictions, and social media addiction has also increased the likelihood of experiencing depression [ 31 ]. These studies imply that distressing situations and social media addiction have mutually strengthened each other, especially after the COVID-19 pandemic.

Although these studies focused on micro-level factors, such as depression or distress, the effect of macro-level social environmental factors on social media addiction is understudied and must be further investigated [ 18 ]. Based on Sun et al. [ 18 ]’s suggestion, we focused on a social environmental factor (i.e., relational mobility [ 32 ]) and investigated the relationship between the social environment and social media addiction.

We conducted two studies to examine the effect of the social environment (i.e., relational mobility) on social media addiction. Relational mobility of a society refers to how easily people in the society can select new relationship partners when necessary [ 32 ]. Relational mobility is lower in typical rural areas wherein interpersonal relationships are closed to outsiders. As relational mobility affects the sensitivity of an individual to social rejection [ 33 , 34 ], it can affect their interpersonal behavior. As an example of interpersonal behavior on social media, we focused on a message exchange situation and examined whether the estimation of reputational damage incurred by ignoring messages moderates the relationship between relational mobility and social media addiction.

Fig 1 illustrates our conceptual model. In Study 1, we examined the following mediation process: people in lower relational mobility societies estimate that greater reputational damage is incurred by message ignorance, which strengthens their social media addiction. This model was proposed based on previous studies that have indicated that people in lower relational mobility societies are more sensitive to social rejection [ 33 , 34 ], and they make decisions based on their estimations of others’ attitudes [ 35 ].

PPT PowerPoint slide
PNG larger image
TIFF original image

https://doi.org/10.1371/journal.pone.0300681.g001

In Study 2, we additionally examined the following dual paths: (1) the mediating effect of estimated reputational damage on the negative relationship between relational mobility and social media addiction and (2) the mediating effect of extroversion, the motivation to expand the social network, and loneliness on the positive relationship between relational mobility and social media addiction. We focused on extroversion, motivation to expand social network, and loneliness because they are related to both relational mobility and social media addiction.

In the following section, we review studies on relational mobility, develop our hypotheses, and outline our contributions.

1.2. Relational mobility and social media addiction

We first focused on the social environmental factor of relational mobility [ 32 ], which is a sociological variable that refers to “the amount of opportunities people have in a given society or social context to select new relationship partners when necessary” [ 32 ]. Relational mobility is lower in typical rural areas, where people exclusively develop intimate relationships with neighbors and seldom construct new relationships with outsiders, whereas it is higher in typical urban areas, where people have weaker social ties and entering or leaving relationships is easier.

Thus, people in low relational mobility societies cannot easily construct alternate relationships, even when they earn a bad reputation and are excluded from their communities. Therefore, the consequences of earning a bad reputation are worse for people in low relational mobility societies [ 36 ], which strengthens their need to avoid reputational damage. Indeed, people in lower relational mobility societies are more sensitive to social rejection [ 33 , 34 ] and refrain from sharing personal information, such as embarrassing experiences or failures [ 37 ].

Based on these studies, we assume that people in lower relational mobility societies are more strongly addicted to social media. Additionally, as they are more sensitive to social rejection, they have more difficulty ignoring messages on social media and thus spend more time on social media, resulting in higher addiction.

Hypothesis 1 : People in low relational mobility societies are more strongly addicted to social media.

1.3. Mediating effect of estimated reputational damage

People in lower relational mobility societies estimate greater reputational damage incurred by ignoring messages on social media because, as described in the Section 1.2., they are more sensitive to social rejection [ 33 , 34 ]. In some cases, this sensitivity might result in an overestimation of reputational damage, leading them to unnecessarily respond to messages on social media. However, in other cases, this sensitivity could reduce the possibility that they underestimate the damage in situations where ignoring them could lower their reputation, and mistakenly ignore messages and damage their reputation. Therefore, estimating greater reputational damage and refraining from ignoring messages are adaptive in that this estimation ( overestimation in some cases) can lower the possibility of damaging their reputation. However, this estimation can strengthen their addiction to social media. Because forming and maintaining strong and stable interpersonal relationships is important to humans [ 38 ], people make decisions based on their estimations of others’ attitudes [ 35 ]. For example, people are more likely to follow norms when they estimate that deviating from them will tarnish their reputation [ 39 ]. Based on these studies, we assumed that people in lower relational mobility societies estimate greater reputational damage incurred by ignoring messages, which strengthens their social media addiction.

Hypothesis 2 : The estimation of greater reputational damage incurred by ignoring messages mediates the negative relationship between relational mobility and social media addiction.

1.4. Accuracy of reputational damage estimation

Hypothesis 2 focuses on the mediating effect of estimated reputational damage. In this section, we examine two questions: do people in both high and low relational mobility societies accurately estimate reputational damage?

Based on [ 36 ], we hypothesized that people in low relational mobility societies overestimate the possibility of earning a bad reputation. Overestimation of reputational damage can help them avoid situations wherein they mistakenly estimate that performing detrimental actions would not damage their reputation when it would. An example of this situation on social media is that people mistakenly estimate that ignoring messages will not tarnish their reputation, even if it does. If they ignore messages based on this underestimation, they will gain a bad reputation, the cost of which is higher in lower relational mobility societies. Therefore, people in lower relational mobility societies are more likely to overestimate reputational damage, especially because interpersonal relationships in these societies are closed and the cost of earning a bad reputation is higher. Indeed, Iwatani and Muramoto [ 39 ] focused on community activities, such as cleanup drives, and demonstrated that people in low relational mobility societies overestimate the possibility of gaining a bad reputation, whereas those in high relational mobility societies estimate it accurately. In this study, we extend their findings to the context of social media and investigate the following hypothesis:

Hypothesis 3 : People in low relational mobility societies overestimate the possibility of receiving a bad evaluation by ignoring messages, whereas people in high relational mobility societies do not.

1.5. Contributions of this study

We believe our study has at least three original contributions. First, it highlights the effects of the social environment on social media addiction. The human mind, including cognition, emotion, and motivation, is affected by both cultural [ 40 ] and social environments [ 41 ]; therefore, social environmental factors can impact social media addiction. However, few studies have investigated the effect of social environments on social media addiction [ 42 ] (however, see [ 43 ], wherein the effect of relational mobility on problematic Internet use was investigated). The novelty of this study lies in its investigation of social media addiction from the perspective of socio-ecological psychology.

Second, we focus on interpersonal interactions between social media users as an antecedent of social media addiction, whereas previous studies have primarily focused on individual psychological factors [ 15 – 17 ]. The originality of our study lies in the fact that we focus on miscommunications between social media users, that is, inaccurately (over)estimated reputational damage as an antecedent factor of social media addiction.

Third, our study also features originality for studies on socio-ecological psychology in that we investigate the effect of relational mobility on online behavior. Previous studies have demonstrated the effect of relationality on general trust, self-esteem, and intimacy with close friends [ 44 ]. However, few studies have demonstrated the effect of offline relational mobility on the online psychological tendencies of humans, except for Dong et al. [ 43 ] and Thomson et al. [ 45 ], who examined the effect of relational mobility on problematic Internet use or online privacy concerns of people.

We conducted two studies to examine the following hypotheses: (1) the direct effect of the social environment (relational mobility) on social media addiction, (2) the mediating effect of reputational damage estimation on the relationship between the social environment and social media addiction, and (3) the accuracy of reputational damage estimation.

2. Materials and methods

2.1. study 1.

We tested the proposed hypotheses using the LINE message exchange service, which is the most popular social media platform in Japan, wherein it is used by approximately 80% of the Internet users [ 46 ]. LINE was considered suitable for this study because it provides a "read notification function," through which users can determine whether their messages have been read and ignored. Additionally, they are aware that their messaging partner can determine if their messages are ignored.

The experiment included two conditions: (1) wherein participants ignored messages (ignorer condition) and (2) wherein participants’ messages were ignored (ignored condition). In the ignorer condition, participants imagined a scenario wherein they read the messages received on LINE and ignored them. They estimated how message senders would evaluate the participants when participants themselves ignored messages (estimated reputational damage). In the ignored condition, participants imagined a scenario wherein they sent messages and the receiver read and ignored them, and evaluated the receiver who ignored the messages (actual reputational damage). We compared the estimated and actual reputational damage and investigated whether people from lower relational mobility societies estimated more reputational damage than the actual damage. We also investigated whether people from lower relational mobility societies estimated higher reputational damage and were more strongly addicted to social media.

2.1.1. Participants.

This study was approved by the Ethics Review Committee of the University of Tokyo. Written informed consent was obtained from all participants. They were recruited through a crowdsourcing service (Yahoo! Crowdsourcing; https://crowdsourcing.yahoo.co.jp/ ) between February 19 and 20, 2022. They were informed of the purpose of this study, and only those who agreed to participate (i.e., those who clicked “agree”) proceeded to answer the questions. Study 1 included 715 participants.

Study 1 was conducted using the between-participant design. We excluded 54 participants who did not pass the instructional manipulation check [ 47 ], 126 who did not use LINE, and 27 who had no friends whom they could contact privately through LINE. We also excluded data with missing values and one participant who answered that their age was 3. Finally, we analyzed the data of 453 participants (males: 308, females: 138, and others: 7). Their average age was 46.46 years ( SD = 11.17).

Half of the participants were randomly assigned to the ignorer condition, and the other half were randomly assigned to the ignored condition, resulting in 222 and 231 participants assigned to the ignorer and ignored conditions, respectively.

We examined whether the sample size was sufficiently large using G*Power version 3.1.9.7 [ 48 ] to conduct a post-hoc power analysis, assuming f = 0.05 (small to medium effect size), α = 0.05, N = 453, and three predictors (condition, relational mobility, and the interaction between them). The calculated power of the test was 0.99, which indicated that the sample size was adequate.

2.1.2. Reputational damage estimation (ignorer condition).

The participants were first asked to write the first-name initials of one of their friends they had privately contacted. The friend’s name is denoted as Mr. A in this study (It was denoted as “A-san” in our actual question). Participants assigned to the ignorer condition were asked to read and imagine a scenario wherein they received a message from Mr. A that stated that they had to discuss something, read it, but did not reply for two or three days.

After participants read the scenario, they estimated Mr. A’s evaluation of them by answering the following six items, extracted from a previous study [ 49 ], on a six-point Likert scale, ranging from 1 (“strongly disagree”) to 6 (“strongly agree”): “Mr. A would think you are a bad person,” “Mr. A would think you are an untrustworthy person,” “Mr. A would think you are an honest person,” “Mr. A would think they do not want to be your friend anymore,” “Mr. A would think they cannot feel secure with you,” and “Mr. A would think you are a cunning person.” We calculated the reputational damage estimation score by averaging the sum of the scores (α = 0.89, M = 2.89, SD = 0.99).

2.1.3. Participants’ evaluation (ignored condition).

Participants assigned to the ignored condition were asked to read and imagine a scenario wherein they sent a message to Mr. A stating that they had to discuss something, Mr. A received and read it but did not reply for two or three days.

After reading the scenario, participants answered questions regarding their evaluation of Mr. A. The items were almost the same as those in the previous scenario, and only the subjects were changed. For example, we changed the item “Mr. A would think you are a bad person” to “I think Mr. A is a bad person.” We again calculated the evaluation score by averaging the sum of the scores (α = 0.89, M = 2.26, SD = 0.88).

2.1.4. Social media addiction.

We used the social media addiction questionnaire (SMAQ; 7-point scale), which is composed of eight items and was proposed by Hawi and Samaha [ 10 ]. We changed the term “social media” in SMAQ to “LINE” for this study. For example, the question “I often think about social media when I am not using it” was modified to “I often think about LINE when I am not using it.” As in [ 10 ], we calculated the social media addiction score by averaging the sum of the scores (α = 0.86, M = 2.73, SD = 1.02). As there was no threshold to distinguish between those addicted to social media and those who were not [ 10 ], we did not perform threshold-based distinguishing between those who were addicted to LINE and those who were not. Participants were considered to be more strongly addicted to LINE if they scored higher on this scale.

2.1.5. Relational mobility.

Relational mobility was measured using the relational mobility scale [ 32 ]. Participants were presented with 12 statements and asked how much they agreed with them based on a six-point Likert scale, from 1 (“strongly disagree”) to 6 (“strongly agree”). The statements included the following: “they (people in the immediate society (your school, workplace, town, neighborhood, etc.) in which you live) have many chances to get to know other people.” The relational mobility score was calculated by averaging the sums of the scores (α = 0.75, M = 3.60, SD = 0.51). The relational mobility of the participant’s society was considered to be higher if they scored higher on this scale.

2.2. Study 2

Although Study 1 only investigated the factors that mediate the negative relationship between relational mobility and social media addiction, Study 2 investigated the factors that mediate the positive relationship between the two. We focused on the following three factors: loneliness, extroversion, and the motivation to expand social network. We examined whether these three factors mediated the positive relationship between relational mobility and social media addiction, which would cancel out the negative relationship examined in Study 1.

First, we focused on loneliness. We assumed that the relationship between relational mobility and loneliness was positive based on the study by Oishi et al. [ 50 ], which demonstrated that people in mobile conditions (wherein they imagined that they would move to a different location every other year) experienced more loneliness than those in stable conditions (wherein they imagined that they would stay in the same city for at least ten years). Additionally, there is a positive relationship between loneliness and social media addiction [ 23 ], which suggests that loneliness mediates a positive relationship between relational mobility and social media addiction.

Next, we focused on extroversion. There is a positive relationship between the within-state migration level and extroversion [ 51 ], which implies that there is a positive relationship between relational mobility and extroversion. Additionally, there is a positive relationship between extroversion and social media addiction [ 17 ]. These findings suggest that extroversion mediates the positive relationship between relational mobility and social media addiction.

Finally, we focus on the motivation to expand social network. An experimental study demonstrated that people in the mobile condition are more motivated to expand their social networks than those in the stable condition [ 50 ]. In addition, extraverts have larger social networks, which can promote their use of social media [ 4 ]. These findings suggest that the motivation to expand the social network mediates the positive relationship between relational mobility and social media addiction. In summary, we developed the following additional hypotheses and examined the model presented in Fig 1 .

Hypothesis 4a : Loneliness mediates the positive relationship between relational mobility and social media addiction.
Hypothesis 4b : Extroversion mediates the positive relationship between relational mobility and social media addiction.
Hypothesis 4c : Motivation to expand social network mediates the positive relationship between relational mobility and social media addiction.

2.2.1. Participants.

This study was approved by the Ethics Review Committee of the University of Tokyo. Written informed consent was obtained from all the participants. Study 2 employed the within-participants design and included 1826 participants, recruited through the same crowdsourcing service (Yahoo! Crowdsourcing; https://crowdsourcing.yahoo.co.jp/ ) between August 26 and 27, 2022. They were informed of the purpose of this study, and only those who agreed to participate proceeded to answer the questions. We excluded 143 participants who did not pass the instructional manipulation check [ 47 ], 309 who did not use LINE, and 209 who had no friends whom they could contact privately through LINE. We also excluded data with missing values and eventually analyzed the data of 1065 participants (males: 670, females: 374, and others: 21). Their average age was 48.11 years ( SD = 12.09).

We examined whether the sample size was sufficiently large by conducting a post-hoc power analysis, assuming a root mean square error of approximation (RMSEA) in the null hypothesis = 0.05, RMSEA in the alternative hypothesis = 0.01, degrees of freedom = 7, N = 1065, and α = 0.05. The calculated power was 0.85, which indicated that the sample size was adequate.

2.2.2. Measurements.

As in Study 1, participants were asked to write the first-name initials of their friends they had privately contacted, who were denoted as Mr. A. Thereafter, they were asked to imagine the following scenarios: (1) wherein they ignored messages and (2) wherein their messages were ignored.

2.2.3. Reputation damage estimation.

The participants read the same scenario as in Study 1 (ignorer condition), wherein they ignored Mr. A’s message, and answered the following five items on a six-point Likert scale based on a previous study [ 49 ], ranging from 1 (“strongly disagree”) to 6 (“strongly agree”): “Mr. A would think you are a bad person,” “Mr. A would think you are an untrustworthy person,” “Mr. A would think they cannot feel secure with you,” “Mr. A would think you are an unreliable person,” and “Mr. A would not want to deepen their friendship with you.” We calculated the reputational damage estimation score by averaging the sums of the scores (α = 0.96, M = 3.16, SD = 1.19).

2.2.4. Participants’ evaluation.

Next, the participants read the same scenario as in Study 1 (ignored condition), wherein Mr. A ignored their messages. Thereafter, they responded with their evaluations of Mr. A. These items were almost the same as those mentioned above, and only their subjects were changed. For example, we changed the item “Mr. A would think you are a bad person” to “I think Mr. A is a bad person.” We calculated the evaluation score by averaging the sum of the scores (α = 0.96, M = 2.68, SD = 1.14).

2.2.5. Social media addiction.

We used the same questionnaires as in Study 1 to calculate the social media addiction scores. The sum of the scores were averaged (α = 0.86, M = 2.69, SD = 1.03).

2.2.6. Extroversion.

We measured extroversion using the Ten-Item Personality Inventory assessment [ 52 ]. The participants were asked to answer the following two items on a seven-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”): “I see myself as extraverted, enthusiastic” and “I see myself as reserved, quiet” (reverse-scored item). We calculated the extroversion score by averaging the sums of the scores ( r = 0.47, p < 0.01, M = 3.55, SD = 1.31). A participant was considered more extraverted if they scored higher.

2.2.7. Motivation to expand social network.

We measured the motivation to expand the social network using a seven-point Likert scale [ 50 ]. The questionnaire was composed of four items (e.g., “eager to make friends,” “want to meet new people”). We calculated the motivation to expand the social network by averaging the sum of the scores (α = 0.92, M = 3.52, SD = 1.37). Participants were considered to have higher motivation to expand their social networks if they scored higher on this scale.

2.2.8. Loneliness.

We measured loneliness using a five-point Likert scale [ 53 ]. The scale was composed of six statements (e.g., “I usually sense an experience of emptiness,” “I often feel missing close people around me”). We calculated the loneliness score by averaging the sum of the scores (α = 0.85, M = 2.82, SD = 0.76). Participants were considered to be lonelier if they scored higher on this scale.

2.2.9. Relational mobility.

As stated in Section 2.1.5, relational mobility score was measured using the relational mobility scale [ 32 ]. It was calculated by averaging the sum of the scores (α = 0.78, M = 3.66, SD = 0.53).

2.3. Statistical analysis

We used R version 4.3.2 for statistical analyses. For mediation analyses, multiple regression analysis, and generalized linear mixed model analysis, the statistical significance standard was set as p = .05, whereas for structural equation modeling (SEM), the statistical significance standard for the model fit was set as RMSEA = .05.

Study 1 was conducted using a between-participants design ( ignorer and ignored conditions). To test Hypotheses 1 and 2, we analyzed participants in the ignorer condition, (i.e., those who answered reputational damage estimation) and conducted a mediation analysis using the bootstrap method (5000 samples) to examine whether the negative effect of relational mobility on social media addiction was mediated by reputational damage estimation. This analysis was conducted after centering all variables in the model.

For testing Hypothesis 3, we conducted a multiple regression analysis. The evaluation was predicted using a dummy evaluator variable ( ignored condition (i.e., participants’ actual reputational damage) = 0, ignorer condition (i.e., estimated reputational damage from others) = 1), relational mobility, and the interaction between them. This analysis was also conducted after centering all variables in this model.

Study 2 employed a within-participant design. Participants read both the ignorer and ignored condition scenarios. For testing Hypothesis 2 and Hypotheses 4a–c, we employed SEM techniques and examined the following hypotheses: estimation of greater reputational damage mediates the negative relationship between relational mobility and social media addiction, whereas loneliness, extroversion, and motivation to expand social networks mediates the positive relationship between them ( Fig 1 ).

For testing Hypothesis 3, we used a generalized linear mixed model with random intercepts for the participants to examine our hypothetical model. The evaluation toward the ignorer was predicted using the dummy evaluator variable ( ignored condition = 0, ignorer condition = 1), relational mobility, and the interaction between them. This analysis was conducted after centering all variables in this model.

3. Results and discussion

3.1. study 1, 3.1.1. are people in lower relational mobility societies more addicted to social media.

First, we examined Hypotheses 1 and 2: (1) people in low relational mobility societies are more strongly addicted to social media (Hypothesis 1) and (2) the estimation of greater reputational damage incurred by ignoring messages would mediate the negative relationship between relational mobility and social media addiction (Hypothesis 2).

We only analyzed the answers of participants in the ignorer condition because we did not measure the estimated reputational damage in the ignored condition. After centering all variables in the model, we conducted a mediation analysis using the bootstrap method (5000 samples) to examine whether the effect of relational mobility on social media addiction was mediated by reputational damage estimation. Relational mobility had a significant effect on reputational damage estimation, indicating that people in low relational mobility societies estimated a higher reputational damage caused by ignoring messages (β = -0.15, p = 0.04). The effect of reputational damage estimation on social media addiction was not statistically significant (β = 0.14, p = 0.06). Additionally, the direct effect of relational mobility on social media addiction was not significant (β = -0.10, p = 0.18). These results did not support Hypotheses 1 and 2. However, consistent with Hypothesis 2, there was a statistically significant negative correlation between relational mobility and reputational damage estimation ( r = -0.15, p = 0.03) as well as a statistically significant positive correlation between reputational damage estimation and social media addiction ( r = 0.16, p = 0.02), although there was no statistically significant correlation between relational mobility and social media addiction ( r = -0.12, p = 0.07).

Thereafter, we conducted an additional analysis using age and sex (male = 0, female = 1) as control variables. To examine the effect of sex, seven participants who did not answer “male” or “female” were excluded. The effect of relational mobility on reputational damage estimation was statistically significant (β = -0.14, p = 0.05). In contrast to the aforementioned analysis, the effect of reputational damage estimation on social media addiction was also statistically significant (β = 0.15, p = 0.04). The direct effect of relational mobility on social media addiction was not significant (β = -0.09, p = 0.28) as in the above analysis. Additionally, the main effect of sex (β = 0.14, p = 0.03) was statistically significant, whereas that of age (β = -0.09, p = 0.23) was not.

3.1.2. Do people in low relational mobility societies overestimate reputational damage?

Next, we examined Hypothesis 3: people in low relational mobility societies overestimate the possibility of receiving a bad evaluation incurred by ignoring messages, whereas people in high relational mobility societies do not.

Prior to the analysis, we constructed a dummy variable for the evaluator (participants’ actual reputational damage = 0, estimated reputational damage from others = 1). After centering all variables in this model, we conducted a multiple regression analysis. The evaluation was predicted using the dummy evaluator variable, relational mobility, and the interaction between the two.

The main effect of evaluator was significant (β = 0.31, p < 0.01), but the interaction effect between the evaluator and relational mobility was not (β = -0.02, p = 0.60). These results imply that people in low relational mobility societies estimate greater reputational damage incurred by ignoring messages than the actual damage (consistent with Hypothesis 3), and the same holds true for people in high relational mobility societies (inconsistent with Hypothesis 3).

The main effect of relational mobility was also significant (β = -0.13, p < 0.01), which implies that (1) people who ignored messages were evaluated more negatively in low relational mobility societies than in high relational mobility societies and (2) people in low relational mobility societies estimated that ignoring messages would incur higher reputational damage than people in high relational mobility societies.

We conducted an additional analysis using age and sex (male = 0, female = 1) as control variables. To examine the effect of sex, seven participants who did not answer “male” or “female” were excluded. The results were the same as those obtained in the aforementioned analysis. The main effects of the evaluator (β = 0.31, p < 0.01) and relational mobility (β = -0.12, p = 0.01) were statistically significant, whereas the interaction effect between the evaluator and relational mobility was not (β = -0.03, p = 0.56). Additionally, the main effects of age (β = -0.08, p = 0.07) and sex (β = -0.02, p = 0.68) were not significant.

3.1.3. Moderating effect of age.

We exploratively examined the moderating effect of age; whether the effect of reputational damage estimation on social media addiction differed depending on age. A multiple regression analysis was conducted after entering all variables in the model. Social media addiction was used as the dependent variable, whereas relational mobility, reputational damage estimation, sex, age, and the interaction between reputational damage estimation and age were used as independent variables. The main effects of reputational damage estimation (β = 0.15, p = 0.02) and sex (β = 0.14, p = 0.04) were statistically significant, whereas those of relational mobility (β = -0.08, p = 0.22) and age (β = -0.09, p = 0.20) were not. Additionally, the interaction effect was not significant (β = 0.01, p = 0.84).

3.1.4. Discussion.

In Study 1, we examined (1) the effect of relational mobility on social media addiction (Hypothesis 1), (2) mediating effect of reputational damage estimation on the relationship between relational mobility and addiction (Hypothesis 2), and (3) accuracy of reputational damage estimation (Hypothesis 3).

First, we found that both relational mobility and reputational damage estimation had no effect on social media addiction; these results do not support Hypothesis 1. Second, we found that people in lower relational mobility societies estimated higher reputational damage. We also found a positive correlation between reputational damage estimation and social media addiction. These results were consistent with Hypothesis 2 (the mediating effect of reputational damage estimation), but this hypothesis was not supported because no direct relationship between relational mobility and social media addiction was observed. These results imply that other factors mediate the positive relationship between relational mobility and social media addiction, which negates the negative relationship between them. We further examined this possibility in Study 2. Third, we found that people overestimate the reputational damage caused by ignoring messages. This result partially supported Hypothesis 3, in that people in low relational mobility societies overestimate reputational damage incurred by ignoring messages, but contradicted Hypothesis 3, in that people in high relational mobility societies also overestimate it.

3.2. Study 2

3.2.1. relationship between relational mobility and social media addiction..

We investigated the relationship between relational mobility and social media addiction using SEM techniques and examined the following possibilities: estimation of greater reputational damage mediates a negative relationship between relational mobility and social media addiction (Hypothesis 2), whereas (2) loneliness, extroversion, and motivation to expand social networks mediate a positive relationship between relational mobility and social media addiction (Hypotheses 4a, 4b, and 4c; Fig 1 ). However, the model did not fit the data (RMSEA = 0.24).

In this model, we focused on three factors that would mediate the positive relationship between relational mobility and social media addiction (Hypotheses 4a, 4b, and 4c): loneliness, extroversion, and the motivation to expand the social network. Indeed, extroversion and the motivation to expand the social network were significantly and positively correlated with social media addiction ( r = 0.12, p < 0.01; r = 0.28, p < 0.01), but loneliness was not ( r = 0.04, p = 0.23).

Therefore, we focused only on the motivation to expand the social network, as it had the strongest correlation with social media addiction. We used SEM techniques and examined the following possibilities: (1) estimation of greater reputational damage incurred by ignoring messages mediates the negative relationship between relational mobility and social media addiction (Hypothesis 2) and (2) motivation to expand social network mediates the positive relationship between them (Hypothesis 4c). This model fit the data (RMSEA = 0.05; Fig 2 ). We conducted an additional analysis using age and sex (male = 0, female = 1) as control variables. To examine the effect of sex, we excluded 21 participants who did not answer “male” or “female.” The result was the same as that of the aforementioned analysis (RMSEA = 0.05; Fig 2 ). The effect of sex on social media addiction was statistically significant (β = 0.08, p = 0.01), whereas that of age was not (β = 0.01, p = 0.85).

Note**: p < 0.01; the values in parentheses indicate the results of the additional analyses with covariates (i.e., age and gender).

https://doi.org/10.1371/journal.pone.0300681.g002

Based on these results, we conducted a dual mediation analysis using 1000 bootstrap samples. The results indicated that reputational damage estimation mediated the negative relationship between relational mobility and social media addiction (indirect effect = -0.01, p = 0.03), whereas the motivation to expand social network mediated the positive relationship (indirect effect = 0.04, p < 0.01; Fig 3 ). We also conducted an additional analysis using age and sex (male = 0, female = 1) as control variables, and obtained same result as that in the aforementioned analysis; reputational damage estimation mediated the negative relationship between relational mobility and social media addiction (indirect effect = -0.01, p = 0.03), whereas the motivation to expand social network mediated the positive relationship (indirect effect = 0.04, p < 0.01; Fig 3 ). These results support Hypotheses 2 and 4c.

https://doi.org/10.1371/journal.pone.0300681.g003

When we used extroversion or loneliness instead of motivation to expand the social network, the models did not fit the data (RMSEA = 0.12 and 0.20, respectively). The models did not fit when entering the control variables (age and gender), either (RMSEA = 0.09 and 0.14, respectively).

3.2.2. Do people in low relational mobility societies overestimate their reputational damage?

Next, we examine Hypothesis 3. We conducted a generalized linear mixed model analysis in Study 2, although we conducted a multiple regression analysis in Study 1. This is because Study 2 employed the within-participants design, whereas Study 1 employed the between-participants design.

Prior to the analysis, we constructed a dummy variable for the evaluator (participants’ actual evaluations = 0, evaluation from others = 1). After centering all variables in the model, we used a generalized linear mixed model with random intercepts for participants to examine our hypothetical model. The evaluation was predicted using the dummy evaluator variable, a relational mobility variable, and the interaction between the two.

The main effect of the evaluator was significant (β = 0.20, p < 0.01), but the interaction effect between the evaluator and relational mobility was not (β = 0.01, p = 0.49). These results imply that people in low relational mobility societies estimate that greater reputational damage will be incurred by ignoring messages than the actual damage (consistent with Hypothesis 3), and the same hold true for people in high relational mobility societies (inconsistent with Hypothesis 3).

The main effect of relational mobility was also significant (β = -0.10, p < 0.01), which implies that (1) people in low relational mobility societies evaluated those who ignored messages more negatively than those in high relational mobility societies, and (2) people in low relational mobility societies also estimated that ignoring messages would incur higher reputational damage than those in high relational mobility societies.

We conducted an additional analysis using age and sex (male = 0, female = 1) as control variables and obtained the same results as those in the aforementioned analysis. The main effects of the evaluator (β = 0.20, p < 0.01) and relational mobility (β = -0.10, p < 0.01) were statistically significant, whereas the interaction effect between them was not (β = 0.01, p = 0.50). Additionally, the main effects of age (β = -0.04, p = 0.14) and sex (β = -0.02, p = 0.39) were not significant.

3.2.3. Moderating effect of age.

We exploratively examined the moderating effect of age; whether the effect of reputational damage estimation on social media addiction and that of the motivation to expand the social network differs depending on age. We conducted a multiple regression analysis after centering all variables in the model. Social media addiction was used as the dependent variable, whereas relational mobility, reputational damage estimation, the motivation to expand the social network, sex, age, the interaction between reputational damage estimation and age, and the interaction between the motivation to expand the social network and age as independent variables. The main effects of reputational damage estimation (β = 0.15, p < 0.01) and the motivation to expand the social network (β = 0.30, p < 0.01) were statistically significant, whereas those of relational mobility (β = -0.02, p = 0.42), age (β = 0.00, p = 0.90), and sex (β = 0.05, p = 0.07) were not. Additionally, the interaction effect between reputational damage estimation and age was not statistically significant (β = -0.01, p = 0.70), whereas that between the motivation to expand the social network and age was statistically significant (β = -0.06, p = 0.03). The effect of the motivation to expand the social network was greater among younger participants ( M - 1 SD ; β = 0.36, p < 0.01) than among older ( M + 1 SD ; β = 0.24, p < 0.01).

3.2.4. Discussion.

Study 2 hypothesized that (1) reputational damage estimation mediates the negative relationship between relational mobility and social media addiction, whereas (2) loneliness, extroversion, and the motivation to expand the social network mediate the positive relationship. We hypothesized that these two mediations would cancel each other out; therefore, there will be no relationship between relational mobility and social media addiction.

We tested these hypotheses using SEM; however, they were not supported. We additionally tested another model that focused only on the motivation to expand the social network: (1) reputational damage estimation mediates the negative relationship between relational mobility and social media addiction and (2) motivation to expand the social network mediates the positive relationship. This model was supported, which implies that the factors promoting social media addiction differ between high and low relational mobility societies. Reputational damage estimation strengthens social media addiction in low relational mobility societies, whereas the motivation to expand social networks strengthens it in high relational mobility societies.

3.3. General discussion

3.3.1. summary of results..

We focused on message exchanges on social media and investigated the effect of social environment (relational mobility) on social media addiction. In Study 1, we examined the following hypotheses: (1) people in low relational mobility societies are more strongly addicted to social media (Hypothesis 1), (2) the estimation of greater reputational damage incurred by ignoring messages mediates the negative relationship between relational mobility and social media addiction (Hypothesis 2), and (3) people in low relational mobility societies overestimate the possibility of receiving a bad evaluation, whereas people in high relational mobility societies do not. In Study 2, we additionally examined (4) loneliness, extroversion, and the motivation to expand the social network mediate the positive relationship between relational mobility and social media addiction (Hypotheses 4a, 4b, and 4c).

Hypotheses 1 and 2 were not supported; we conducted a mediation analysis but observed no relationship between relational mobility and social media addiction or between reputational damage estimation and social media addiction. In contrast, when we conducted the correlational analyses, although we found no statistically significant correlation between relational mobility and social media addiction, we found a statistically significant negative correlation between relational mobility and reputational damage estimation, as well as a statistically significant positive correlation between reputational damage estimation and social media addiction. These results are partially consistent with Hypothesis 2 and imply that other factors mediate the positive relationship between relational mobility and social media addiction, which might negate the negative relationship hypothesized in Hypothesis 1.

Therefore, we additionally examined this possibility in Study 2 (Hypotheses 4a–c), which was partially supported: reputational damage estimation mediated the negative relationship between relational mobility and social media addiction, whereas the motivation to expand the social network mediated the positive relationship. This result supports Hypotheses 2 and 4c.

Additionally, we found a statistically significant main effect of sex in both Studies 1 and 2 in that females were more strongly addicted to social media than males were. Chen et al. [ 54 ] demonstrated a difference between sexes in the factors associated with smartphone addiction. They found that females were more likely to be addicted to smartphones as they used social networking services, whereas this relationship was not found for males. Although it is only a speculation, our study demonstrated that females were more strongly addicted to social media, partially because we focused on LINE, a social media especially for connecting with others.

The explorative analysis in Study 2 demonstrated an interesting interaction effect between age and the motivation to expand the social network. Those with a higher motivation to expand the social network were more strongly addicted to social media, and this effect was smaller among older people ( M + 1 SD ) than younger ones ( M - 1 SD ). This may be partially because social media usage does not expand the social networks among older people as much as among younger people. According to Kojima (2022) [ 55 ], the rate of those who use LINE every day was lower among older people (50s male: 56.3%; 50s female: 69.9%) than among young people (20s male: 76.2%; 20s female: 86.8%). Even when older people try to send messages to their friends through social media, their friends may not use social media. Future research should focus on the differences in the social media environments between various age groups.

In contrast to Hypothesis 4a, loneliness did not mediate a positive relationship between relational mobility and social media addiction. We found no relationship between loneliness and social media addiction ( r = 0.04, p = 0.23). This result is inconsistent with previous studies that have demonstrated a positive relationship between loneliness and social media addiction [ 23 ]. This non-significant relationship was surprising in that the COVID-19 pandemic would have strengthened the relationship between loneliness and social media use. Kayis et al. [ 56 ] demonstrated that the fear of COVID-19 strengthened loneliness, which in turn strengthened smartphone addiction. Although speculative, the capacity to be alone might have weakened the relationship between loneliness and addiction. As the capacity to be alone is negatively related to social media addiction [ 57 ], even when individuals feel lonely, if their capacity to be alone is significant, they will not be strongly addicted to social media. We also found that loneliness was significantly negatively correlated with relational mobility ( r = -0.26, p < 0.01), which is inconsistent with the results obtained by Oishi et al. [ 50 ], who found that participants felt lonelier when they were asked to imagine a situation wherein they frequently moved to a different location. Although relational mobility is high in societies in which people move frequently [ 58 ], this is not always the case. Even in such societies, some people have fewer opportunities to construct new relationships. This might be the reason that our results, which focused on relational mobility, were inconsistent with those obtained by Oishi et al. [ 50 ].

Moreover, in contrast to Hypothesis 4b, when we used SEM techniques and examined the following hypotheses, the model did not fit the data (RMSEA = 0.12). Although the model did not fit the data, the direction of each path was statistically significant and consistent with our hypotheses: (1) people in a lower relational mobility society estimated greater reputational damage (β = -0.21, p < 0.01), which strengthened their social media addiction (β = 0.14, p < 0.01), whereas (2) people in a higher relational mobility society were more extraverted (β = 0.40, p < 0.01), which strengthened their social media addiction (β = 0.12, p < 0.01). A reason why our model did not fit the data was the weak relationship between extroversion and social media addiction. Although some studies have demonstrated a positive relationship between extroversion and social media addiction [ 17 ], others have indicated no relationship between them [ 15 ]. Future studies should examine the factors that moderate the relationship between extroversion and social media addiction.

We also found no relationship between age and addiction in this study. This may be because, compared with studies that have focused on university students [ 28 , 31 , 54 ], the percentage of younger participants in our studies was low. Studies 1 and 2 included 6.84 and 6.20% of individuals in their 20s, respectively, and 18.10 and 17% in their 30s, respectively. Additionally, we did not recruit minors (aged < 18 years). This may be a reason for us not finding a relationship between age and social media addiction.

We also examined the accuracy of reputational damage estimation incurred by ignoring messages, as hypothesized in Hypothesis 3, which was partially supported: people in low relational mobility societies overestimate the reputational damage incurred by ignoring messages. This was consistent with Hypothesis 3. Meanwhile, people in high relational mobility societies also overestimated the reputational damage incurred by ignoring messages, which was inconsistent with Hypothesis 3.

3.3.2. Practical implications.

Our results suggest that the antecedent factors of social media addiction differ between high and low relational mobility societies, which implies that interventions for moderating social media addiction differ between high and low relational mobility societies. We demonstrated that people in higher relational mobility societies had a higher motivation to expand social networks, which strengthened their social media addiction. Therefore, moderating this motivation could be effective in preventing social media addiction.

In contrast, we also demonstrated that people in lower relational mobility societies estimated greater reputational damage incurred by ignoring messages, which strengthened their social media addiction. This reputational damage was overestimated. Other relevant studies have found that people who are highly sensitive to rejection are more likely to perceive others’ ambiguous behaviors as intentional rejections [ 59 ] and are more strongly addicted to social media [ 60 ]. These studies, as well as our results, imply that people overestimate the possibility of being rejected or reputational damage incurred by ignoring messages, which can strengthen their social media addiction. Therefore, correcting this estimation can be effective for lowering social media addiction, especially among those in lower relational mobility societies or those more sensitive to rejection.

One specific intervention can be to provide them with feedback that ignoring messages does not lower their reputation as much as they estimate. For example, by asking students in a class to evaluate those who ignore messages on social media and by showing them the distribution or average of the evaluation score, they would notice that they overestimate the reputational damage. This type of intervention has previously succeeded in changing behavior, such as reducing college students’ alcohol consumption [ 61 ]. Students used to excessively consume alcohol based on the incorrect estimation that other students prefer alcohol [ 62 ], but by correcting their inaccurate estimation (i.e., by informing them that other students do not prefer alcohol as much as they estimated), their alcohol consumption was decreased [ 61 ]. Although these studies were conducted 30 years ago and did not involve social media, we believe that modifying or correcting the estimation of reputational damage can be a novel and important intervention to lower addiction as it focuses on interpersonal miscommunications between social media users, which differs from other interventions that focus on individual aspects (e.g., asking users to reflect on “what social media they used, how long and how they used the social media, their thoughts and emotions related to their social media use” [ 5 ]).

3.3.3. Limitations and future work.

This study had several limitations. First, it only included participants from Japan. We assumed that even in Japan, different cities have different degrees of relational mobility. Indeed, some studies have surveyed people in Japan and demonstrated the effect of relational mobility on their attitudes [ 63 , 64 ]. However, Japanese people do not necessarily live in high relational mobility societies because relational mobility in Japan is low [ 44 ]. This might be the reason that, inconsistent with the prediction of Hypothesis 3, people from both high and low relational mobility societies overestimated the possibility of earning a bad reputation by ignoring messages. Additional studies must be conducted in higher relational mobility societies, such as the United States, to investigate our hypotheses.

Second, we used a crowdsourcing service to recruit participants from both high and low relational mobility societies. However, it has been demonstrated that some participants recruited via crowdsourcing services answer questions carelessly [ 65 ], which might have affected our results. Although we performed an instructional manipulation check and excluded those who did not pass it, additional studies are required to ensure the robustness of our results.

Finally, our study did not sufficiently investigate the effects of COVID-19 on distress or social media addiction. Some studies have focused on the psychological distress caused by the COVID-19 pandemic and demonstrated that it strengthened social media addiction [ 20 , 30 ]. Therefore, a possible intervention for moderating social media addiction is to lower psychological distress. Tambelli et al . [ 66 ] surveyed late adolescents (aged between 18 and 25 years) and demonstrated that those who felt a greater sense of security from their parents or peers exhibited lower COVID-19-related distress. Lowering distress by constructing good relationships with parents or peers could weaken social media addiction, at least among late adolescents.

4. Conclusion

This study demonstrated that the antecedents of social media addiction differ between high and low relational mobility societies. In Study 1, we demonstrated that people in low relational mobility societies estimate greater reputational damage, but there was no direct relationship between relational mobility and social media addiction. Therefore, in Study 2, we additionally explored the factors that mediate the positive relationship between relational mobility and social media addiction. The results indicated that (1) people in lower relational mobility societies expect higher reputational damage, which strengthens their social media addiction; and (2) people in high relational mobility societies are more motivated to expand their social networks, which strengthens their social media addiction. In addition, both studies demonstrated that people expect greater reputational damage than the actual damage. These results imply that the mechanism of social media addiction differs depending on the social environment: the estimation of reputational damage strengthens social media addiction in low relational mobility societies, whereas the motivation to expand social networks increases social media addiction in high relational mobility societies. Therefore, correcting this damage overestimation would be an effective strategy to moderate social media addiction, especially in low relational mobility societies, whereas reducing the motivation to expand social networks would be effective especially in high relational mobility societies.

View Article
Google Scholar
2. Zou Z, Wang H, d’Oleire Uquillas F, Wang X, Ding J, Chen H. Definition of Substance and Non-substance Addiction. In: Zhang X, Shi J, Tao R, editors. Substance and Non-substance Addiction. Singapore: Springer; 2017. pp. 21–41. https://doi.org/10.1007/978-981-10-5562-1_2
PubMed/NCBI
13. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5-TR. 5th ed., text revision. Washington, DC: American Psychiatric Association Publishing; 2022.
46. ICT Research & Consulting Inc. 2022. Survey of SNS usage trends in 2022. ICT Research & Consulting Inc. 2022 May 17 [Cited 2023 July 22]. Available from: https://ictr.co.jp/report/20220517-2.html/
58. Oishi S, Schug J, Yuki M, Axt J. The Psychology of Residential and Relational Mobilities. In: Gelfand MJ, Chiu C, Hong Y, editors. Handbook of Advances in Culture and Psychology, Volume 5. Oxford University Press; 2015. p. 221. https://doi.org/10.1093/acprof:oso/9780190218966.003.0005

an image, when javascript is unavailable

Jane’s Addiction Concert Ends Abruptly After Perry Farrell Throws a Punch at Dave Navarro, Is Forced Offstage by Crew

Fans posted dramatic video of the escalating confrontation and the enraged singer's removal from the stage.

By Chris Willman

Chris Willman

Senior Music Writer and Chief Music Critic

Kamala Harris Campaign Name-Checks 28 Taylor Swift Titles or Lyrics in Statement Following Trump’s Anti-Taylor Tirade 10 hours ago
Jane’s Addiction Apologizes to Fans for Onstage Fight, Cancels Next Show 1 day ago
A Walk Through ‘MacArthur Park’: Jimmy Webb Tells the History of an Odd Pop Classic That Found New Life as the Musical Climax of ‘Beetlejuice Beetlejuice’ 1 day ago

jane's addiction punch perry farrell dave navarro concert gig

Been caught sparring: A concert by the reunited Jane’s Addiction in Boston came to a sudden end Friday night when a clearly enraged Perry Farrell threw a punch at guitarist Dave Navarro — and was restrained by crew members, still appearing physically agitated as he was hustled offstage.

Sony’s ‘Concord’ Shutdown an Indictment of Live-Service Gaming

UNIVERSAL BASIC GUYS: From FOX’s Emmy Award-winning animation house, Bento Box Entertainment, and Sony Pictures Television, all-new animated comedy UNIVERSAL BASIC GUYS premieres Sunday, Sept 8 (8:00-8:30 PM ET/PT) on FOX. UNIVERSAL BASIC GUYS © 2024 by FOX Media Inc.

'Universal Basic Guys' Is Moronic and Dated: TV Review

Popular on variety.

Video of the lead-up to the scuffle shows Ferrell fiercely grunting in the direction of the audience, before he turns to his right and begins issuing those bellows at Navarro, face to face. He appears to aggressively bump shoulders with the guitarist during a solo, and Navarro eventually stops playing and puts a hand up to Farrell’s chest to establish distance. Then the singer appears to deliver a punch. At that point, as the stage lights are lowered, three men, including bassist Eric Avery, surround and grab hold of Farrell, who is finally forced offstage, still apparently struggling as he’s hustled into the wings.

Once Farrell was gone, the rest of the band — including Navarro — stepped to the front of the stage to give the crowd a gentler farewell, appearing calm as they hugged one another, applauded the audience, tapped their hearts and offered a peace sign.

The altercation immediately blew up on social media, with no shortage of jokes — including countless Oasis comparisons — along with more serious concerns expressed for the well-being of the band members involved.

“Jane’s Addiction broke up before Oasis omg,” came a typical tweet, from the user @Tribecalledflex .

On setlist.fm , the notations section for the Boston show offered an ironic juxtaposition of trivia about the gig, reading: “Note: Stephen Perkins’ drum kit was outfitted with balloons in celebraton of his birthday. The set ended early after Perry Farell punched Dave Navarro.”

Press representatives for Jane’s Addiction and the tour promoter, Live Nation, could not immediately be reached for comment Friday night.

Chatter had already circulated in social media about the Jane’s Addiction shows earlier this week in New York City, held at the Rooftop at Pier 51. At the first of two concerts there, Farrell admitted to the audience he was not in great vocal shape, reportedly saying, “Ladies and gentlemen, I have to be honest with you. Something’s wrong with my voice. I just can’t get the notes out all of a sudden.” The next day, bandmate Eric Avery posted to Instagram, “Looking forward to getting another crack at this spectacular rooftop venue tonight. I’m optimistic we will be better.”

The following night in New York, things indeed took a turn for the better, according to a reviewer for JamBase who wrote, “I had seen the reports of Farrell’s condition on Tuesday, so I had trepidation as Jane’s Addiction came on. All my fears quickly eased away as my first Jane’s experience was a great one. Farrell sang well, Perkins crushed it behind the kit, Navarro shredded and Avery anchored the band with his steady work on bass. … Some of (Farrell’s) stories were engaging and others rambled as he chugged from a bottle of wine. He also was fixated on a device that I’d imagine was supposed to add effects to his voice but didn’t have much impact to my ear. However, when it came to singing the songs, Farrell nailed most of them.”

Ironically, in light of what has since transpired, the singer halted a beef at the band’s second New York show. “Farrell actually stopped the latter tune after he spotted a fight in the audience,” JamBase reported. “Farrell had a fan named ‘Bobby’ make up with the crowd member he was feuding with.”

Jane’s Addiction’s first tour in 15 years is a co-headlining one, with another beloved ’90s group, Love & Rockets, sharing the bill.

Friday night’s fateful Boston show came about 20 dates into the groups’ dual tour itinerary, with 15 left to go. As of this writing, the tour is still scheduled to continue and wrap up Oct. 16 at L.A.’s YouTube Theater, returning to where the group already successfully performed once near the beginning of their outing in mid-August.

More from Variety

‘Emilia Perez,’ ‘The Count of Monte Cristo’ to Be Showcased at the American French Film Festival’s Comeback Edition in Los Angeles

A human hand turning down a handshake from a robot hand

Why Studios Still Haven’t Licensed Movies and TV Shows to Train AI

LAS VEGAS, NEVADA - SEPTEMBER 01: Joey Chestnut attends press conference for LIVE Chestnut vs. Kobayashi: Unfinished Beef at the Hyper X Arena at the Luxor on September 01, 2024 in Las Vegas, Nevada. (Photo by David Becker/Getty Images for Netflix © 2024)

Joey Chestnut Beats Takeru Kobayashi in Netflix’s ‘Unfinished Beef’ Hot Dog-Eating Contest

‘One Piece’ Season 2 Casts ‘Bridgerton’ Actress Charithra Chandran as Miss Wednesday

‘Emily in Paris’ Season Four Part Two Launches in Rome With Cast Posing by the Colosseum and a Fleet of Vintage Fiat 500 Cars Unleashed

More from our brands, emmy awards 2024: see the complete winners list.

What It’s Like to Watch an America’s Cup Race Right on the Water

UFC 306 at Sphere Pulls in Record $22M for UFC, Sphere

The Best Loofahs and Body Scrubbers, According to Dermatologists

Emmys 2024 Review: TV’s Biggest Night Plays It Too Safe With a Timid, Listless Ceremony — Plus, Grade It!

Before Trump, neo-Nazis pushed false claims about Haitians as part of hate campaign

The day after the presidential debate at which former President Donald Trump spread a false story about Haitian immigrants eating pets in Springfield, Ohio, Christopher Pohlhaus, leader of the national neo-Nazi group Blood Tribe, took to his Telegram channel to take credit.

Pohlhaus, a Marine-turned-tattoo artist known as “Hammer” to his hundreds of followers, wrote Blood Tribe had “pushed Springfield into the public consciousness.”

Members of his hate group agreed. “The president is talking about it now,” a member wrote on Gab, a Twitter-like service popular with extremists. “This is what real power looks like.”

Trump’s line at the debate was the culmination of a weekslong rumor mill that appears to have at least been amplified by Blood Tribe, which has sought to demonize the local Haitian community online and in person. The debate drew more than 67 million viewers , according to the media analytics company Nielsen.

As with most rumors, the beginning of the baseless claims about Haitians eating pets in Springfield is hard to pinpoint, but Blood Tribe undoubtedly helped spread it.

Starting in late June, people in local Facebook groups had been posting about Haitian children chasing ducks and geese. Around the same time, conservative media was characterizing Springfield as being “flooded” with Haitian immigrants. Over the next few weeks, the Facebook complaints, still without evidence , got darker, with anonymous posters claiming they were hearing that ducks and geese were going missing, perhaps even being eaten by their immigrant neighbors.

The Springfield Police Division told NBC News that “there have been no credible reports or specific claims of pets being harmed, injured, or abused by individuals within the immigrant community.”

The rumor began to grow legs in the private local groups as the blue-collar city’s immigration-driven population growth became national news in an election year.

Blood Tribe latched on last month when it started posting to Telegram and Gab about Springfield, stoking racist rumors about Haitians and Black people in general eating domestic animals. In a hate-filled Gab post from early September that included multiple racial epithets, the group claimed Haitians “eat the ducks out of the city parks.” The reach of Blood Tribe’s isn’t clear, as its Gab and Telegram accounts have fewer than 1,000 followers.

In response to a request for comment sent to Pohlhaus, Blood Tribe said in an email that it stood by its claims and that it would continue its activism, “making sure” Haitian immigrants “are all repatriated.”

The claims also began circulating in more mainstream conservative spaces, most notably on social media.

A few days after Blood Tribe’s Gab post, an X account not affiliated with Blood Tribe that is popular in conservative circles, @EndWokeness, posted a screenshot of a message board post and a picture of a man appearing to hold a goose. The screenshot purports that Haitians had stolen and eaten a neighbor’s cat, and the message from the X account adds that “ducks and pets are disappearing.” That post has been viewed 4.9 million times, according to X’s public metrics.

The man who originally posted the photo said that it was taken in Columbus, Ohio, and that he didn’t know the person’s ethnicity and he said he didn’t believe the photo should have been used to spread false rumors.

Even so, the post sparked a major jump for the rumor. What had been steady conversation that spread in August was beginning to die out early this month, according to data from Peak Metrics, a company that tracks online threats. But the goose post led to a second wave of virality.

From there, the rumors snowballed . Claims of residents’ pets being stolen, animal sacrifice and voodoo worshiping, as well as discussions about the “great replacement” conspiracy, began to circulate, according to an analysis by Memetica, a digital investigations company.

The memes followed. Artificial intelligence-generated images first circulated on 4chan and then in MAGA communities on X of pets and waterfowl being embraced and protected by Trump, which pushed the conspiracy theories even further into the mainstream. At the height of the spread this week, Trump’s running mate, Sen. JD Vance of Ohio, promoted the baseless rumors on his own X account.

“It’s possible, of course, that all of these rumors will turn out to be false,” Vance posted. But he told his followers, without proof that the rumors weren’t true, they shouldn’t “let the crybabies in the media dissuade you, fellow patriots. Keep the cat memes flowing.”

As the rumors gained steam in conservative online spaces , Blood Tribe was planning real-world actions.

On Aug. 10, about a dozen masked Blood Tribe members carrying banners adorned with swastikas marched in downtown Springfield, labeling the event an “anti-Haitian Immigration march.” On Facebook, Mayor Rob Rue said: “There was an attempt to disrupt our community by an outside hate group. Nothing happened, except they expressed their First Amendment rights.”

Blood Tribe’s Gab account shot back and invited its followers to harass the mayor. “Hello, Springfield Ohio! We hear you have a real problem with Haitian ‘refugees.’”

On Aug. 27, Drake Berentz, the only Blood Tribe member apart from Pohlhaus who marches with his face shown, stood before the Springfield City Commission. Identifying himself by his online moniker, Berentz offered “a word of warning” before his mic was cut off for threatening the commission. He was escorted out by police.

Springfield isn’t Blood Tribe’s first target, and it’s not likely to be its last, said Jeff Tischauser , a senior researcher for the Southern Poverty Law Center who monitors hate groups. Blood Tribe and other hate groups have used the real-world actions for recruitment, attention and intimidation.

More from NBC News

Bomb threat in Springfield spurs fear and closures after false anti-migrant claims
Ohio police have 'no credible reports' of Haitian immigrants' harming pets, contradicting JD Vance's claim
White House denounces 'dangerous' false claims about Haitian immigrants spread by JD Vance

Last year, armed Blood Tribe members rallied at drag events in Columbus and Wadsworth , Ohio, chanting Nazi slogans and waving Nazi salutes. They marched at a Pride event in Watertown, Wisconsin and at the capitol in Madison , and they shouted “Heil Hitler” outside Disney World . This year, abandoning LGBTQ issues for immigration, they have protested in Harrisburg, Pennsylvania; Nashville, Tennessee; Pierre, South Dakota; and Springfield.

“They aim to stoke fear among local communities that they view as potentially friendly to their ideas,” Tischauser said. “Goal No. 1 is psychological trauma, to keep folks out of public life that they disagree with. Number 2 is to create these viral moments for their group to get attention on Gab and on Telegram.”

Blood Tribe, like other white nationalist groups, also seeks to normalize extremist ideas and symbols, Tischauser said. With Trump’s and the wider conservative embrace of the Haitians-eating-pets rumor, Springfield has been a success for the hate groups.

“The GOP seems to be falling into their trap,” Tischauser said. “Groups like Blood Tribe truly see themselves as pushing the GOP further to their position on policy, but also on rhetoric.”

The threat from such a mainstreaming of extremist ideas was on display in Springfield on Thursday. Blood Tribe has used its Gab account to dox Springfield residents and government employees who have spoken out against the recent rumors. City Hall had to close down Thursday after multiple government agencies there got bomb threats.

Brandy Zadrozny is a senior reporter for NBC News. She covers misinformation, extremism and the internet.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
BMC Psychol

The relationship between social networking addiction and academic performance in Iranian students of medical sciences: a cross-sectional study

Seyyed mohsen azizi.

1 Clinical Research Development Center of Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran

Ali Soroush

Alireza khatony.

2 Social Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran

3 Nursing Department, School of Nursing and Midwifery, Dowlat Abad, Kermanshah, Iran

Associated Data

The identified datasets analyzed during the current study are available from the corresponding author on reasonable request.

Social networks have had a major influence on students’ performance in recent years. These networks create many opportunities and threats for students in various fields. Addiction to social networking and its impact on students’ academic performance caused the researcher to design and conduct this study. The purpose of this study was to investigate the relationship between social networking addiction and academic performance of students in Iran.

In this cross-sectional study, 360 students were enrolled by stratified random sampling. The study tools included personal information form and the Bergen Social Media Addiction Scale. Also, the students’ overall grade obtained in previous educational term was considered as the indicator of academic performance. Data were analyzed using SPSS-18.0 and descriptive and inferential statistics.

The mean social networking addiction was higher in male students (52.65 ± 11.50) than in female students (49.35 ± 13.96) and this difference was statistically significant ( P < 0.01). There was a negative and significant relationship between students’ addiction to social networking and their academic performance (r = − 0.210, p < 0.01).

Conclusions

The social networking addiction of the students was at moderate level and the male students had a higher level of addiction compared to the female students. There was a negative and significant relationship between the overall use of social networks and academic performance of students. Therefore, it is imperative that the university authorities take interventional steps to help students who are dependent on these networks and, through workshops, inform them about the negative consequences of addiction to social networks.

Introduction

In recent years, significant changes have taken place around the world regarding the quantitative and qualitative expansion of internet, social networks and number of people who use them. Social networks include websites and applications that allow users to share content, ideas, opinions, beliefs, feelings, and personal, social, and educational experiences. They also allow communication between a wide range of users at global level [ 1 , 2 ]. Instagram, Telegram, Facebook, Twitter, Skype, and WhatsApp are among the most popular and commonly used virtual social networks [ 3 – 8 ]. Currently (2018), the number of internet users in the world is about 4.021 billion and also 3.196 billion people use social networks on a regular basis worldwide [ 9 ]. Iran is one of the developing countries where internet and social networks have grown significantly. The use of social media has tripled over the past three years, and more than 47 million Iranians are using social networks, according to the Iranian Center of Statistics [ 10 ].

Social networks play a crucial role in learning environments as a key communicational channel and a source of social support [ 11 ]. Many social networking websites, such as Edmodo, are specifically designed for learning [ 12 ]. Social networks have many advantages in learning as they provide wide access to information and information resources, reduce barriers to group interaction and telecommunications [ 13 ], support collaborative learning activities [ 14 ], encourage learners to learn more about self-learning [ 15 ], increase engagement and learner’s motivation [ 16 ], enhance engagement of learners with each other and their teachers [ 17 ] and support active and social learning [ 15 ]. In general, the emergence of new technologies such as internet and social networks, in addition to providing opportunities in facilitating and improving the quality of global communications, has created some threats [ 18 ]. When the use of social networks is managed poorly, they can have negative consequences at the individual and social levels. Social networking addiction is one of the consequences that many social network users may experience [ 19 ]. Thus, the extensive use of social networks is a new form of soft addiction [ 20 ].

There are many different theories about the addiction to internet and social networks. The most important theories include dynamic psychology theory, social control theory, behavioral explanation, biomedical explanation, and cognitive explanation. According to dynamic psychology theory, the roots of social networking addiction are in the psychological shocks or emotional deficiencies in childhood, personality traits, and psychosocial status. According to the social control theory, since addiction varies in terms of age, sex, economic status, and nationality, certain types of addiction are more likely to be found in certain groups of society than in other groups [ 21 ]. The theory of behavioral explanation believes that, a person uses social networks for rewards such as escaping reality and entertainment. Based on the biomedical explanation theory, the presence of some chromosomes or hormones, or the lack of certain chemicals that regulate brain activity, are effective in addiction [ 22 , 23 ]. According to the cognitive explanation theory, social networking addiction is due to faulty cognition, and people tend to use social networks to escape from internal and external problems [ 24 ]. In general, addiction to social networking is classified as a form of cyber-relationship addiction [ 25 ].

Social networking addiction refers to mental concern over the use of social networks and the allocation of time to these networks in such way that, it affects other social activities of individuals such as occupational and professional activities, interpersonal relationships and health [ 19 ] leading to disruption of their life [ 20 ].

Social networking has a negative impact on physical and psychological health and causes behavioral disorders [ 26 ], depression [ 27 , 28 ], anxiety and mania [ 28 ]. In this regard, results of a study on German students (2017) showed a positive relationship between addiction to facebook, with narcissism character, depression, anxiety and stress [ 29 ]. It is believed that addiction to social networking is higher in people with anxiety, stress, depression and low self-esteem [ 4 ]. Grifith (2005) suggests that addictive behavior is a behavior that has certain characteristics such as salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse [ 30 ]. Addictive behavior refers to repeated habits that increase the risk of a disease or social problems in a person. Over the past decade, addictive behaviors, such as overuse of internet or social networks, have become a part of everyday life of students. Social networking addiction includes the characteristics such as ignoring the real problems of life, neglecting oneself, mood swing, concealing addictive behaviors, and having mental concerns [ 4 ].

In this regard, signs and symptoms of addiction to social networking can include experiencing disturbances in day-to-day work and activities, spending more than one hour a day on social networks, being curios to see the old friends’ profiles, ignoring work and daily activities due to the use of social networks, and feeling anxious and stressed due to the lack of access to social networks [ 31 ].

Evidence suggests that many factors are associated with addiction to internet and social networks. Among these factors are online shopping, dating, gaming and entertainment, using mobile phones for access to internet, searching for pornographic images, user personality trails, and low self-esteem [ 19 , 30 , 32 – 34 ].

Students are one of the most important users of the virtual world and social networks. The overuse of social networks has positive and negative academic, social, and health consequences for the students [ 35 ]. Reduced academic performance is one of the most important consequences of social networking overuse for students. The results of a study on medical students showed that students who used social networks and internet more than average had a poor academic achievement and low level of concentration in the classroom [ 36 ]. The results of another study on Qatari students showed that Grade Point Average (GPA) was lower among students who were addicted to social networking compared to other students [ 37 ]. The results of a study in India showed that internet and social networking addiction had a negative effect on academic performance and mental health of students [ 38 ]. The results of a Korean study revealed a negative correlation between the use of internet for non-academic purposes and academic performance of students [ 39 ]. Findings of a study in Iran (2018) also showed a significant correlation between addiction to the internet and educational burnout [ 40 ].

Thus, considering the key role of students in promoting the quality of physical and mental health of society, and also due to the lack of knowledge on the type of relationship between social networking addiction and academic performance of the students of medical sciences in Kermanshah University of Medical Sciences (KUMS), the present study was designed and implemented. The purpose of this study was to investigate the extent of social networking addiction among the students of medical sciences and its relationship with academic performance of the students.

Thus, we sought to examine the following hypothesis in this study:

1) There is significant relationship between the mean of social networking addiction and students gender.

2) Social networking addiction have a negative and significant correlation with academic performance.

This descriptive-analytical and cross-sectional study was conducted between June and August 2018.

Sample and sampling method

The research population consisted of all students who were studying at KUMS in the second semester of 2017–2018 academic years. The criteria for entering the study included; studying at the second semester of 2017–2018 academic year, studying at the second semester or above, willing to participation in the study, and completing the questionnaires fully. Stratified random sampling was performed. To calculate the sample size, the result of Masterz’s study (2015) was used [ 41 ], according to which, addiction to Facebook, Twitter and YouTube social networks was 14.2, 33.3, and 47.2, respectively. If we assume that, the prevalence of social networking addiction is about 33.3%, then the sample size will be 340 individuals considering 10% drop out of the samples. Thus, in the present study, in order to increase the stability and accuracy of the results, 360 participants using random sampling method were entered into the study.

Instruments

The study tools included a personal information form and the Bergen Social Media Addiction Scale (BSMAS). The information form had 5 questions about gender, age, educational level, school of study, and Grade Point Average (GPA). BSMAS was designed by Andreassen et al. (2012) at the University of Bergen [ 42 ]. The reliability coefficient of this questionnaire has been confirmed by the Cronbach’s Alpha method (alpha = 0.8), [ 42 ] and its internal consistency has been calculated to be 0.88 [ 43 ]. The psychometric properties of the Persian version of the BSMAS using confirmatory factor analysis and Rasch models on 2676 students by quota sampling, have been reviewed and approved in Iran by reporting the indexes such as X 2 = 86.52 ( P < 0.001), CFI = 0.993, Average variance extracted = 0.51, and composite reliability = 0.86 [ 44 ]. In the present study, the reliability coefficient of the questionnaire for internal consistency was 0.88 using Cronbach’s Alpha method.

BSMAS consists of 18 questions and 6 items, in a way that, each item has 3 questions. The items include; salience [ 1 – 3 ], tolerance [ 4 – 6 ], mood modification [ 7 – 9 ], withdrawal [ 10 – 12 ], relapse [ 13 – 15 ] and conflict [ 16 – 18 ]. Salience refers to our thinking and behavior in using social networks. It means that, the addictive use of social networks is manifested in the form of individual’s dependency on social networks. Tolerance (craving) represents a gradual increase in the use of social networks to gain pleasure. Mood modification represents modifying and improving behavior or mood. In other words, this component suggests that some users use social networks to get rid of unpleasant feelings. Withdrawal is an unpleasant feeling that a person experiences when disconnected from social networks or discovers he or she is forbidden to use social network. Relapse is a failed attempt of a person to control his/her social networking usage. Conflict represents issues that cause tensions in relationships with others, workplace, education, etc. [ 42 , 43 ].

The questions in this scale are in 5-point Likert scale, including very rarely [ 1 ], rarely [ 2 ], sometimes [ 3 ], often [ 4 ] and very often [ 5 ], which are scored from 1 to 5, respectively. The minimum score in the Social Networking Scale is 18 and the maximum score in 90. In our study, the average response time to the questionnaire was about 20 min. The questionnaires were distributed in faculties at the end of the classes. The sampling lasted for one month.

In this study, the samples were categorized in one of the following categories according to the score they obtained from the questionnaire: Normal use of social networks (0–19), mild social networking addiction [ 20 – 35 , 43 , 45 – 47 ], moderate social networking addiction (40–69) and severe social networking addiction (70–90), [ 48 ]. GPA was used to assess the academic performance of students.

Data collection

At first, the study permission was obtained from the KUMS’s Research Deputy. Then, the researcher attended the Department of Education at the faculties of KUMS, including the faculties of Medicine, Para medicine, Dentistry, Pharmacy, Nursing and Midwifery and Health, and received a list of students from each faculty. The list was numbered and then, based on random number table method, samples were selected. The researcher referred to the students based on their classroom schedule and, if they were interested in participating in the study, invited them to enter the study. If any student did not want to participate in the study, he/she was replaced by the next or pervious person in the list. The objectives of the study were explained to all samples and then the questionnaires were given to them to be complete. The questionnaires were collected after the completion.

Data analysis

Data were analyzed by 18th version of the Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA) and two levels of descriptive and inferential statistics. The data normality was first evaluated using Kolmogorov-Smirnov test, which indicated an abnormal distribution of variables of social networking addiction and GPA. Spearman’s correlation coefficient was used to examine the correlation between the social networking and GPA. To compare the social networking addiction scores in terms of nominal qualitative variables (such as sex), the Mann-Whitney U test was used, and in terms of ordinal qualitative variables (such as education level and school) and quantitative variables (such as age and group), Kruskal-Wallis H test was used. p -value of less than 0.05 was considered as significant level.

Ethical consideration

The University’s Ethics Committee approved the study with the code: IR.KUMS.REC.1397.077. The goals of study were explained to the samples and written informed consent was obtained from all of them. Concerning the confidentiality of personal information and responses, reassurance was given to the participants.

Of the 360 students participating in the study, 199 students (55.3%) were female and the rest were male. The mean age of the participants was 25.48 ± 3.39 years and they were mainly at the age range of between 21 and 30 years old. Also, 46.7% of the students ( n =168) were undergraduate and most of them were studying at the faculty of dentistry ( n =101, 28.1%), (Table (Table1 1 ).

Comparison of mean and standard deviation of social networking addiction score in terms of demographic characteristics

Variable		(%)	Mean (SD)	- value
Sex	Male	161 (44)	52.65 (11.50)	0.001
Sex	Female	199 (55.3)	49.35 (13.96)
Age group	≤20	19 (5.3)	53.78 (14.95)
	21–30	310 (86.1)	51.54 (15.98)	NS
	31–40	31 (8.6)	50.57 (11.45)
Age (years), mean (SD)	25.48 ± 3.39	–	–
Educational level	Undergraduate	168 (46.7)	52.8 (12.70)
	Postgraduate	126 (35.0)	50.61 (12.75)	NS
	Ph.D.	66 (18.3)	48.03 (13.95)
School	Medicine	41 (11.4)	51.73 (16.05)
	Paramedical	95 (26.4)	53.49 (12.53)
	Dentistry	101 (28.1)	50.43 (11.73)	NS
	Pharmacy	44 (12.2)	48.54 (12.54)
	Nursing and Midwifery	50 (13.9)	48.08 (13.67)
	Health	29 (8.9)	50.41 (12.84)

* Non-significant

The mean social networking addiction was 50.83 ± 13.00 out of 90, which was at moderate level. Most of the students had moderate addiction (254 students and 70.6%), (Table (Table2). 2 ). The addiction to social networking in the male students was significantly higher than female students ( p ≤ 0.01), (with the mean and standard deviation of 52.65 ± 11.50 and 49.35 ± 13.96, respectively). In term of age, the highest and lowest levels of social networking addiction were related to age groups of less than 20 years old and 31 to 40 years old (with the mean of 53.78 ± 14.95 and 50.57 ± 11.45, respectively), which showed no statistically significant difference. Undergraduate and PhD students had the highest and lowest level of addiction, respectively, and did not have statistically significant difference (with the mean and standard deviation of 52.8 ± 12.70 and 48.03 ± 13.95, respectively). In terms of school, the highest and lowest levels of addiction were related to the students of Para medicine and nursing and midwifery schools, respectively (with a mean and standard deviation of 53.49 ± 12.53 and 48.08 ± 13.67, respectively), and this difference was not statistically significant. (Table (Table1). 1 ). There was a negative and significant correlation between social networking addiction and academic performance ( p ≤ 0.01, r = − 0.210) of the students. Also, there was a negative and significant correlation between all the subscales of social networking addiction and GPA (Table (Table3 3 ).

Intensity of social networking addiction in participants

Intensity of social network addiction	(%)
Natural use	7(1.9)
Mild addiction	57(15.8)
Moderate addiction	254(70.6)
Severe addiction	42(11.7)

The correlation between social networking addiction and GPA in study samples

variables	GPA
variables		-Value
Salience	− 0.148	0.005
Tolerance	− 0.133	0.012
Mood modification	−0.171	0.001
Relapse	−0.215	0.000
Withdrawal	−0.164	0.002
Conflict	−0.205	<0.001
Total of social networking addiction	−0.210	<0.001

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed)

In our study, the rate of addiction to social networking was moderate. In this regard, the prevalence of social networking addiction among students in Singapore and India was reported to be 29.5 and 36.9% respectively [ 26 , 28 ]. The results of a meta-analysis study (2018) on internet addiction showed that, the prevalence of internet addiction among medical students was 30.1% worldwide [ 49 ]. Results of a meta-analysis study (2017) suggest that, the prevalence of internet addiction in Iran is moderate [ 50 ]. Social networking addiction increases the incidence of disorders such as depression, stress and anxiety [ 28 , 29 ]. If students fail to manage the time they spend on social networks and the reasons for doing that, they will be seriously harmed at individual and social levels. Accordingly, the result of a study showed that the overuse of social networks affects the social life of individuals [ 51 ]. Hawi and Samaha (2016) argued that, the higher the social networking addiction of students, the lower their self-esteem is [ 52 ]. The use of social networks has become an integral part of the lives of many students, because they introduce them to a world of different possibilities, especially in their field of study. However, these networks are like double-edged knives. If students do not manage the use of these networks, they will be addicted to them, and will have to face different consequences, especially in relation to their education.

Based on our findings, the first hypothesis of the study was confirmed and a statistically significant relationship was found between social networking addiction and students’ gender. In this regard, we found that the mean of social networking addiction in male students was significantly higher than female students. This part of our findings is consistent with the findings of other studies [ 26 , 33 , 34 , 36 , 45 ]. In studies conducted on students of medical sciences in Iran, internet addiction in male students was higher than female students [ 33 , 53 , 54 ]. Findings of a study in Turkey (2016) suggested that addiction in Tweeter social network among male students was higher than female students [ 55 ]. But the results of a study on Polish students showed that, female students were using Facebook more than male students [ 46 ], Andreassen et al. (2017) showed that being female is one of the factors that has a statistically significant relationship with social networking addiction [ 43 ]. According to the social control theory, since addiction varies in terms of demographic variables such as sex, certain types of addiction are more likely to be found in certain groups of society than in other groups [ 21 ]. In this regard, evidence suggests that in general, 68% of women and 62% of men use social networks, and on average, women spend 46 min and men spend 31 min on social networking [ 52 ].

Based on the findings, the second hypothesis of the research was confirmed and a negative and significant correlation was found between social networking addiction and students’ academic performance. This finding means that, an increase in the excessive use of social networks decreases the academic performance. Based on the theory of behavioral explanation, a person enters social networks for rewards such as escaping reality and entertainment [ 21 ]. Excessive use of these networks can cause addiction in the user. Our results are consistent with the findings of Ahmadi and Zeinali (2018), Kumar et al. (2018), and Kim et al. (2018) studies [ 38 , 39 , 56 ]. In this regard, Ahmadi and Zeinali (2018) in a study showed that social networking addiction has a negative impact on academic achievement by creating academic procrastination, reducing sleep quality and increasing academic stress [ 56 ]. However, Junco et al. (2011) believed that some social networks such as Twitter can be used as a learning tool by students and professors. Also, these networks can increase academic engagement in students and professors [ 57 ]. But the point about the use of social networks as an educational tool is that, overuse of social networks reduces the level of academic engagement and students’ grades. Therefore, when using social networks, special attention should be paid to the time management. In fact, improving students’ academic performance depends on the lesser use of social networks [ 58 ]. Evidence suggests that excessive use of social media such as Facebook is associated with a significant level of stress and this stress, negatively affects the student’s academic performance [ 59 ]. Uncontrolled use of social media reduces the study time, which has a negative effect on the academic performance of students. Also, since people who spend many hours around the clock using social media do not have enough rest and suffer from fatigue and sleep disruption, these can have a negative impact on their concentration and learning [ 60 ]. Reducing the quality of sleep, negatively affects the students’ concentration and academic quality. Additionally, reducing the duration of sleep may interfere with the secretion of serotonin and melatonin, and this increases the level of stress and anxiety of students. As a result, these hormonal changes reduce brain function and cognitive abilities [ 56 ]. In line with these studies, evidence indicates a positive and significant correlation between inappropriate and problematic use of technology and educational problems [ 61 , 62 ]. In fact, the over-use of social networks will result in failure in education and social relationships, and also leads to ineffective time management. Social media is not self-destructive and harmful on its own, but rather it is the way of using it that leads to positive and negative consequences. The proper use of social media requires a culture and awareness of how they should be used correctly. In this regard, the results of a study indicated that universities that use this technology can motivate students in the specialized field to help them be effective and positive. Increasing students’ motivation can lead to progress in different areas, especially education [ 63 ]. Despite this issue, some university professors and lecturers still oppose the use of social networks by students [ 64 ]. In our opinion, the increasing expansion of social networks has provided opportunities and unique conditions for the growth and improvement of students’ academic status, but they should be used sensitively and managed properly, because due to the attractiveness of various social networks, it is possible to get addicted to them.

Limitations

Our study had several limitations. Due to the cross-sectional nature of this study, it was not possible to explain the causal relationships between the variables of social networking addiction and academic performance of students. In the current study, the data were collected by self-reporting method that could have affected the accuracy of the results. However, the researcher tried to solve this limitation by reassuring the participants that their responses would remain confidential.

Practical implications

Since students, who have a high level of anxiety, stress, and depression and a low level of self-esteem, are more at risk of social networking addiction, designing and implementing counseling programs to promote mental health is recommended for them. Additionally, Cognitive Behavioral Therapy (CBT) is suggested to reduce social networks dependency. CBT is one of the most effective therapies for reducing social networks dependency. Based on the CBT method, thoughts are the determinant of emotion, therefore, by controlling negative thoughts and managing behavior, we can reduce the dependence on social networks.

The level of social networking addiction of the students was moderate, and male students had a higher level of addiction to social networking than female students. A significant and negative relationship was found between the social networking addiction and GPA. Considering the negative effects of social networking on students’ academic performance, the issue of addiction to social networking should be comprehensively reviewed and considered. Also, appropriate planning should be made to prevent addiction to social networking, control its use, and increase the opportunities and reduce the threats of this tool. In this regard, allocating some of the research priorities to the positive and negative applications of social media at individual, social and academic levels can be beneficial. Given the importance of addiction to social networking and its potentially destructive impact on students’ academic performance, similar studies are recommended in other universities and in different fields to obtain a more conclusive result. In this regard, the use of mix methods can help to better understand the phenomenon of addiction to social networking and its relationship with the academic performance of students.

Acknowledgments

This work was supported by the deputy of research and technology of KUMS [grant numbers 97067). We would like to express our sincere gratitude to all the students who participated in this research. We highly appreciate the Clinical Research Development Center of Imam Reza Hospital for their wise advices.

The study was funded by Kermanshah University of Medical Sciences. Grant number is 97067.

Availability of data and materials

Abbreviations.

BSMAS	Bergen Social Media Addiction Scale
GPA	Grade Point Average
KUMS	Kermanshah University of Medical Sciences
SPSS	Statistical Package for the Social Sciences

Authors’ contributions

Ak, AS and SA designed the study and wrote the protocol. AS conducted literature searches and provided summaries of previous research studies. SA conducted the statistical analysis. Ak and SA wrote the first draft of the manuscript and all authors contributed to and have approved the final manuscript .

Ethics approval and consent to participate

The study was approved by research ethics committee of Kermanshah University of Medical Sciences with the code: IR.KUMS.REC.1397.077. The written informen consent was obtained from all the participants.

Consent for publication

No Applicable.

Competing interests

The authors declare there are no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Seyyed Mohsen Azizi, Email: moc.oohay@izizaneshoms .

Ali Soroush, Email: [email protected] .

Alireza Khatony, Phone: +9838279394, Email: moc.liamg@ynotahkA , Email: ri.ca.smuk@ynotahkA .

IMAGES

Social Media Addiction by Kean Lai on Prezi
Social media (short video type) addiction model
Social Media Addiction: 11 Signs, Causes, Tips To Break It
Essay on Social Media Addiction
Social Media Addiction Presentation by Jerome Kuek on Prezi
Social media addiction by Er khai ghee on Prezi

VIDEO

What is the Parasite Hypothesis Of Addiction? #foryou #podcast #clips
Sahulat
Using Hypothesis Social Annotation with Large Courses, Part 2
The Social Psychology of The Furry Fandom
Fact Based Speculation, Sebastian Rogers Case
Unemployment in India

COMMENTS

A review of theories and models applied in studies of social media
For example, the rational addiction theory implies that individuals decide to continuously engage in excessive social media use after evaluating the benefits and drawbacks of the behavior; however, they may hold biased perceptions when making judgements and overestimate the value of social media, especially when social media use become ...
A review of theories and models applied in studies of social media
In this study, we reviewed 25 distinct theories/models that guided the research design of 55 empirical studies of social media addiction to identify theoretical perspectives and constructs that have been examined to explain the development of social media addiction. Limitations of the existing theoretical frameworks were identified, and future ...
A theory of social media dependence: Evidence from microblog users
This study uses a theory-guided approach and seeks to clarify the development of psychological dependence in the context of social media, with a particular focus on microblogging. Building on the theory of rational addiction, this study hypothesizes that dependence is initially developed from habit. Furthermore, the study draws on the cognitive ...
(PDF) Social Media Addiction: A Systematic Review through Cognitive
As a result, social media addiction, a type of behavioral addiction related to the compulsive use of social media and associated with adverse outcomes, has been discussed by scholars and ...
A review of theories and models applied in studies of social media
Terms, such as social media addiction, problematic social media use, and compulsive social media use, are used interchangeably to refer to the phenomenon of maladaptive social media use characterized by either addiction-like symptoms and/or reduced self-regulation (Bányai et al., 2017, Casale et al., 2018, Klobas et al., 2018, Marino et al ...
Exploring the mechanism of social media addiction: an empirical study
The problematic use of social media progressively worsens among a large proportion of users. However, the theory-driven investigation into social media addiction behavior remains far from adequate. Among the countable information system studies on the dark side of social media, the focus lies on users' subjective feelings and perceived value.
Understanding the mechanism of social media addiction: A socio
Social media addiction has become alarmingly serious among numerous users and led to considerable psychological and behavioral issues. This study examines the formation of addiction, with a particular focus on university students, to gain a great understanding of how social media addiction works.
Social Media Addiction
Social media has become a common term for Social Networking Sites (SNS), which are platforms that mainly focus on facilitating content sharing and social interaction by users (Pellegrino et al., 2022).Social media has become near ubiquitous in the modern age serving as a hub for the formation of online communities, entertainment, self-presentation, information, and communication.
Predictors of social networking service addiction
SNS addiction can be defined as an excessive, compulsive use of social media platforms that interferes with daily life, leading to negative consequences in physical, social, and mental well-being 11.
Using Theoretical Models of Problematic Internet Use to Inform
Theory of rational addiction + cognitive-affective-behaviour paradigm: Modelling study: Social media - microblogs: None described • Desire to increase utility of internet use• Habitual use • Cognitive distortions about usage• Negative anticipation of non-usage impact on affect: None described: Wei et al. (2017) (2017) Tripartite ...
A biopsychosocial approach to understanding social media addiction
Fourth, we propose social comparison as another social predictor of social media addiction. Social comparison theory (Festinger, 1954) suggests that people are driven to assess themselves through comparison with others. We get a sense of our abilities and self-worth from comparing ourselves to those who are better than us, through upward ...
Research trends in social media addiction and problematic social media
The relations among social media addiction, self-esteem, and life satisfaction in university students: Hawi and Samaha: Social Science Computer Review: 2017: 149: 29.8: 7: Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale: Monacis et al. Journal of Behavioral ...
Risk Factors Associated With Social Media Addiction: An Exploratory
Excessive and compulsive use of social media may lead to social media addiction (SMA). The main aim of this study was to investigate whether demographic factors (including age and gender), impulsivity, self-esteem, emotions, and attentional bias were risk factors associated with SMA. The study was conducted in a non-clinical sample of college ...
2
Media Effects Theories . In this chapter, I define media effects as the deliberate and nondeliberate short- and long-term within-person changes in cognitions, emotions, attitudes, and behavior that result from media use (Valkenburg et al., Reference Valkenburg, Peter and Walther 2016).And I define a (social) media effects theory as a theory that attempts to explain the uses and effects of ...
Why people are becoming addicted to social media: A qualitative study
Social media addiction (SMA) led to the formation of health-threatening behaviors that can have a negative impact on the quality of life and well-being. Many factors can develop an exaggerated tendency to use social media (SM), which can be prevented in most cases. This study aimed to explore the reasons for SMA.
Addiction to social networking sites: Motivations, flow, and sense of
Social media addiction is a behavioral addiction characterized by an uncontrollable and insatiable desire to be ... we have contributed to SNS addiction theory by incorporating the sense of belonging mechanism. This is important because it impacts theory beyond a task-induced psychological state (i.e., flow) to a sense-of-community-induced ...
(PDF) SOCIAL MEDIA ADDICTION AND YOUNG PEOPLE: A ...
In Study 1, we used a survey method with a sample of college students (N = 232) and found that social media addiction was negatively associated with the students' mental health and academic ...
Antecedents of social media addiction in high and low relational ...
Contrary to previous studies on the antecedent factors of social media addiction, we focused on the social environmental factor of relational mobility (i.e., the ease of constructing new interpersonal relationships) and investigated its relationship with social media addiction. People in low relational mobility societies have fewer opportunities to select new relationship partners and ...
Social Media Addiction and its Implications for Communication
and potential for social media addiction, while drawing conclusions about the framework of social media utilization. This thesis will be structured as a literature review, focusing on the potential impact of ... In current research on social media, uses and gratifications theory has not been fully . Addiction. , .
Social Media in Adolescents: A Retrospective Correlational Study on
Go to: Considering the growing interest in the possible effects of internet's addiction on adoles-cent's mental health, this study aimed at exploring the psychological correlates of social media and internet problematic use during the first year of the covid-19 pandemic. A cross-sectional study was conducted in a sample of secondary school ...
Social media addiction: Its impact, mediation, and intervention
social media addiction contributes to lower self-esteem, which, in turn, leads to a decrease in mental health and. academic performance. In other words, self-esteem may play a mediating role in ...
The association between digital addiction and interpersonal
According to this hypothesis, these individuals turn to online IRs to compensate for a lack of offline social interaction. This theory provides an explanation for the relationship between online social interaction and problematic digital device use. ... social media addiction, and internet game addiction (Yang et al., 2022a). This study ...
Jane's Addiction Gig Ends After Perry Farrell Punches Dave Navarro
Chatter had already circulated in social media about the Jane's Addiction shows earlier this week in New York City, held at the Rooftop at Pier 51. At the first of two concerts there, Farrell ...
Before Trump, neo-Nazis pushed false claims about Haitians as part of
The debate drew more than 67 million viewers, according to the media analytics company Nielsen. As with most rumors, the beginning of the baseless claims about Haitians eating pets in Springfield ...
Investigation of the Effect of Social Media Addiction on Adults with
Social media addiction can be defined as a type of psychological dependence that develops through cognitive, sensual, and behavioral processes and produces social, academic, or professional negative results in an individual's life [13,14]. The DSM-5 underlines it as a common and serios medical illness. Excessive or problematic use of the ...
Trump repeats baseless claim about Haitian immigrants eating pets
The claim appears to have come from a number of different sources which have been turned into a cohesive - though baseless - story by pro-Trump social media accounts.
The relationship between social networking addiction and academic
According to the cognitive explanation theory, social networking addiction is due to faulty cognition, and people tend to use social networks to escape from internal and external problems . In general, addiction to social networking is classified as a form of cyber-relationship addiction [ 25 ].

Save citation to file

Add to My Bibliography

A review of theories and models applied in studies of social media addiction and implications for future research

Similar articles

Publication types

Related information

Other Literature Sources

To read this content please select one of the options below:

Design/methodology/approach

Originality/value

Related articles

Report an issue or find answers to frequently asked questions

Social Media Addiction

Access this chapter

Author information

Corresponding author

Rights and permissions

Copyright information

About this entry

Download citation

Predictors of social networking service addiction

Similar content being viewed by others

The association between passive social network usage and depression/negative emotions with envy as a mediator

A taxonomy of technology design features that promote potentially addictive online behaviours

Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective

Literature review

Research model

Positive affect

Negative affect

Social influence

Perceived enjoyment

Methodology

Subjects and data collection

Measurement instrument

Common method bias (CMB)

Measurement model

Structural model

Ethical approval

Informed consent

Consent to participate

Theoretical implications

Practical implications

Limitation and further research

Data availability

Author information

Contributions

Corresponding authors

Ethics declarations

Additional information

Supplementary Information

About this article

Share this article

Quick links

Login Alert

Book contents

2 - Theoretical Foundations of Social Media Uses and Effects

Media Effects Theories

The Selectivity Paradigm

The Transactionality Paradigm

Dispositional Factors

Developmental Factors

Social Context Factors

The Conditionality Paradigm

Dispositional Susceptibility

Developmental Susceptibility

Social-Context Susceptibility

Social Media as a Social Context in Its Own Right

Computer-Mediated Communication Theories

A Transactional Affordance Theory of Social Media Uses

Social Media Users Shape Their Own Effects

Social Media as a Micro- and Mesosystem

It Is the Subjective Experience That Counts

Asynchronicity

Cue Manageability

Scalability

Conclusions and Avenues for Future Research

Save book to Kindle

Save book to Dropbox

Save book to Google Drive

Antecedents of social media addiction in high and low relational mobility societies: Motivation to expand social network and fear of reputational damage