What Makes TikTok so Addictive?: An Analysis of the Mechanisms Underlying the World’s Latest Social Media Craze
Author: Sophia Petrillo
The health impacts of social media addiction remain somewhat unknown. Recent studies indicate variable health effects depending on the severity of the addiction, and increased social media use predicts more significant health consequences. One study investigating the impact of social media addiction on stress among employees of 13 companies in Thailand found that those with a higher degree of addiction appear to have a lower capacity for mindfulness (i.e. the ability to be fully engaged with the present moment). Social media addiction may reduce productivity and success in work, education, and other areas of life. Additionally, the study revealed that individuals experiencing addiction to social media choose emotion-focused coping to alleviate stress rather than problem-focused coping. In contrast to problem-focused coping, in which an individual takes actions targeted at the source of the problem, emotion-focused coping involves efforts to reduce the emotional severity of a situation as a means of resolving the problem. However, the employment of social media to reduce stress qualifies as unhealthy use and may increase emotional exhaustion. Those who are addicted often rely on social media to distract from real-life problems, which masks them and prevents addressing underlying issues. 1
Generally, social media has been linked with several adverse health impacts, particularly in transitional-age youths and adolescents. For example, more frequent daily social media site visits have been associated with higher odds of depression among U.S. individuals between the ages of 19 and 32, and corresponding findings have been reported internationally. 2 Furthermore, two separate studies, one on Scottish adolescents and another on U.S. college students, both indicated a relationship between increased use of social media and heightened levels of anxiety. 3,4 Associations between social media use and poor sleep and unhealthy eating habits have also been supported by nationally- and internationally-based studies. 5,6 Taken together, these findings suggest that the implications of social media addiction can be damaging to both individual and population health.
One social media platform that has seen a significant increase in popularity recently is TikTok. Reminiscent of newly-retired platforms, Vine and Musical.ly, regarding the type and format of app content, TikTok features short-form videos on every topic imaginable. At first featuring lip-synching and dancing to popular songs, current content has expanded to now include comedy, technical skill instruction, fitness inspiration, and myriad other categories. In addition, users can create original content and respond to content made by others through likes, comments, and reshares. Another key component of the app is the “For You” page, a feed specifically curated for each user by the app based on user activity and interaction with other content. Certain individuals have taken advantage of the platform as a marketing tool, establishing themselves as “influencers;” many companies also utilize the app to promote their products and messages. The global audience is heavily skewed towards younger generations, with almost half of its users under age 34, and teenagers make up nearly one-third of accounts. Overall, the platform had over 800 million users in 2019 and is expected to exceed 1 billion users by the end of 2020. Its current economic valuation of $75 million qualifies it as the world’s most valuable startup. Since its popularity spike in 2018, TikTok has surpassed other traditional social media apps such as Instagram and Facebook as the most-downloaded social media app. 7 Clearly, TikTok is well-established, rivaling other platforms for supremacy in the social-media world.
The ‘like’ button is a hallmark of nearly all social media platforms. The action of ‘liking’ social media content has recently become so popular that Merriam Webster now lists an alternative definition of ‘like’ in the dictionary as “to electronically register one’s approval of (something such as an online post or comment) for others to see (as by clicking on an icon designed for that purpose).” 8 The button was first created in 2005 on Vimeo as an alternative way for users to react to videos that felt less concrete than ‘favoriting’ them; its later introduction to Facebook in 2009 and subsequent alterations to its functionality contributed to its establishment as a fixture of social media platforms. 9 ‘Likes’ provide information on social norms and indicate the societal view of particular media that is posted, influencing how individuals perceive it. Additionally, ‘likes’ offer information to social media companies and other websites where there are ‘like’ button plugins so they can more specifically tailor their content to users to keep them more engaged without directly asking their preferences. 10 The ‘like’ button was instrumental in the rapid growth of Facebook in 2010 and has had similar effects on TikTok over the past few years. Thus, although the platforms differ in their content and audiences, they are remarkably alike at the structural level.
In alignment with traditional mechanisms of reward-based learning and facilitation of the habit and addiction loops, ‘likes’ serve as a reward for social media users. A study utilizing a functional MRI paradigm to mimic the “Instagram experience” of viewing “liked” photos demonstrated increased neural activity in regions traditionally associated with reward, namely the nucleus accumbens, and provided evidence for the influence of virtual peer endorsement through ‘likes’ as a form of quantifiable social endorsement among users; accordingly, receipt of a ‘like’ indicates that others approve of an individual’s content. 11 This satisfies the human desire for acceptance by others, particularly those they respect and whose opinions they value; these individuals often comprise one’s ‘friends’ or ‘followers’ on social media. Dopamine release is a key part of the positive feedback loop that drives reward-based learning; increased dopaminergic activity in the brain in response to receiving a ‘like’ encourages future social media use and continued content publication in hopes that the pleasurable experience will re-occur. 12 ‘Likes’ also keep users engaged with social media platforms by representing a form of investment; ‘liking’ content elicits the psychological experience of investment in the platform, and the more invested people are, the more likely they are to care about it and return to the website or app in the future. Evidently, ‘likes’ are gratifying in multiple ways — it feels good to receive likes from other people, and it also feels good to give ‘likes’ to other people in the same way that it feels good to give people gifts. For both forms, the presence of the like button allows instant gratification, which drives habitual use and addiction through positive reinforcement. 13
Undoubtedly, the appeal and entertainment value of content posted on TikTok is a major factor in its popularity. Users are intrigued by videos posted by others and may recreate these videos or publish original content. However, the platform’s success is also heavily influenced by elements of the app itself, and it has been argued that certain app features drive the formation and sustenance of addictions to the platform. Recent reports reveal that users spend an average of 46 minutes per day on the app and open it eight times daily; considering the maximum length of videos is 15 seconds, they may watch upwards of 180 videos per day on average. 14 Like other social media platforms, the infinite scroll and variable reward pattern of TikTok likely increase the addictive quality of the app as they may induce a flow-like state for users that is characterized by a high degree of focus and productivity at the task at hand, 15 whether that be a game, one’s social media feed, or another virtual activity. Once immersed in the flow-like state, users may experience a distorted sense of time in which they do not realize how much time has passed. Furthermore, the app interface itself is straightforward and user-friendly, with only a limited number of buttons and sections of the app for users to navigate, which further enables entrance into “flow.” 16 Videos are short, which is ideal given the decreasing attention capacity of youths in the 21st century. When they play, they consume the entire device screen, which creates an immersive experience for users. 17
The personalized “For You” stream created by artificial intelligence (AI) for each user has also been identified as a key contributor to TikTok addiction. TikTok differs from other social media apps because an individual’s feed is not based on deliberate choices made about the content they want to see. Instead, AI presents individuals with content and uses their reactions to it (in the form of likes, comments, and reshares) to determine other content they might like, facilitating a continuous cycle that starts from the first use and becomes increasingly accurate with repeated engagement. 18 All of the in-app features prolong the time that users spend on the app, which increases the addictive capacity of the platform. To further support this effort, developers constantly change the app layout and add new features so that users spend more time on the app navigating and adjusting to the new design.
Although the similarity may not be immediately evident, analysis of social media apps reveals that they are designed to function like slot machines — the “swipe down” feature required to refresh one’s feed mirrors pulling a slot machine lever, and the variable pattern of reward in the form of entertaining videos on TikTok simulates the intermittent reward pattern of winning or losing on a slot machine; this pattern keeps individuals engaged under the impression that the next play might be “the one.” 19 The striking parallelism between social media apps and slot machines is intriguing given that gambling is the only behavioral addiction currently recognized by the DSM-5. Provided that social media apps are functionally akin to slot machines, it is likely that the use of these apps is just as addictive as slot machines and fosters social media addiction, much like how slot machines contribute to gambling addiction.
Taken together, specific consideration of TikTok in the larger context of social media platforms reveals that “TikTok addiction” is likely a result of a combination of effects. Like other substance and behavioral addictions, it is expected that there are dispositional factors involved in the development of addiction to TikTok. This is because certain lived experiences and personality traits are believed to predict a tendency for engagement in habitual behaviors and addiction. Although these characteristics are often unpreventable, therapeutic and medicative treatments may effectively reduce their influence on an individual’s behavior, so this driver of TikTok addiction may not be too significant.
Unfortunately, it appears that structural and contextual aspects of TikTok are greater contributors to addiction than dispositional attributes of users. Elements of app design and functionality, namely the variable reward pattern of the content stream, the simple, “flow-inducing” interface, and the capability for “endless scroll,” capitalize on classical conditioning and reward-based learning processes to facilitate the formation of habit loops and encourage addictive use. Unlike dispositional drivers of “TikTok addiction,” situational elements of the platform are engineered by app developers, and thus, could be eliminated. However, developers are unwilling to relent with the knowledge that their app’s success depends on its ability to manipulate users to continue use despite any adverse consequences. Although this behavior is conscious and deliberate, whereas dispositional factors are often unconscious and uncontrollable, changing the attitudes and behavior of those in the social media industry may pose a greater challenge to public health efforts to reduce “TikTok addiction” than simply treating misaligned personality traits; this is the reality of living in an increasingly digital and technologically-based world.
- Sriwilai, K., & Charoensukmongkol, P. (2016). Face it, don’t Facebook it: impacts of social media addiction on mindfulness, coping strategies and the consequence on emotional exhaustion. Stress and Health , 32 (4), 427-434.
- Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., … & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and anxiety , 33 (4), 323-331.
- Woods, H. C., & Scott, H. (2016). # Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of adolescence , 51 , 41-49.
- Lee-Won, R. J., Herzog, L., & Park, S. G. (2015). Hooked on Facebook: The role of social anxiety and need for social assurance in problematic use of Facebook. Cyberpsychology, Behavior, and Social Networking , 18 (10), 567-574.
- Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2016). The association between social media use and sleep disturbance among young adults. Preventive medicine , 85 , 36-41.
- Sidani, Jaime E., Ariel Shensa, Beth Hoffman, Janel Hanmer, and Brian A. Primack. “The association between social media use and eating concerns among US young adults.” Journal of the Academy of Nutrition and Dietetics 116, no. 9 (2016): 1465-1472.
- TikTok Revenue and Usage Statistics (2020). (2020, October 30). Retrieved from https://www.businessofapps.com/data/tik-tok-statistics/
- Dictionary by Merriam-Webster: America’s most-trusted online dictionary. (2020). Retrieved from https://www.merriam-webster.com/
- Eranti, V., & Lonkila, M. (2015). The social significance of the Facebook Like button. First Monday , 20 .
- Zara, C. (2019, December 18). How Facebook’s ‘like’ button hijacked our attention and broke the 2010s. Retrieved from https://www.fastcompany.com/90443108/how-facebooks-like-button-hijacked-our-attention-and-broke-the-2010s
- 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
- Burhan, R., & Moradzadeh, J. (2020). Neurotransmitter Dopamine (DA) and its Role in the Development of Social Media Addiction. Journal of Neurology , 11 (7), 507.
- Ghose, T. (2015, January 27). What Facebook Addiction Looks Like in the Brain. Retrieved from https://www.livescience.com/49585-facebook-addiction-viewed-brain.html
- Flynn, Kerry, Kristina Monllos, Lara O’Reilly, and Seb Joseph. “Pitch Deck: TikTok Says Its 27m Users Open the App 8 Times a Day in the US.” Digiday . Published February 26, 2019. https://digiday.com/marketing/pitch-deck-how-tiktok-is-courting-u-s-ad-agencies/.
- Csikszentmihalyi, M. (2002). Flow: The classic work on how to achieve happiness . Random House.
- Montag, C., Lachmann, B., Herrlich, M., & Zweig, K. (2019). Addictive Features of Social Media/Messenger Platforms and Freemium Games against the Background of Psychological and Economic Theories. International journal of environmental research and public health , 16 (14), 2612. https://doi.org/10.3390/ijerph16142612
- Meltzer, D. (2018, February 08). Why Short-Form Video Needs to Be Part of Your Content Strategy. Retrieved from https://www.entrepreneur.com/article/308684
- Knowledge, V. I. (2019). The TikTok Strategy: Using AI Platforms to Take Over the World.
- Liu, R. (2020, September 21). The psychology of why social media is so addictive [Web log post]. Retrieved from https://uxdesign.cc/the-psychology-of-why-social-media-is-so-addictive-67830266657d
- Publishing Policies
- For Organizers/Editors
- For Authors
- For Peer Reviewers
TikTok Addiction Behaviour Among Users: A Conceptual Model and Research Propositions
Social media addiction has become a serious problem and deserve urgent attention. TikTok, one of the emerging social media platforms, has gained popularity among social media users and scholars anticipate that the phenomenon of TikTok addiction is expanding especially among adolescents. Despite this alarming concern, less research attention has been paid to the dark side of TikTok compulsive behaviour. The present article aims to propose a conceptual model to depict the external and internal factors determining addiction behaviour among young users of TikTok. We propose a causal-chain framework originated from the Stimuli-Organism-Response (SOR) paradigm to delineate the role of information quality and system quality (i.e.: the external factors as the stimuli), and flow experience (i.e.: the internal factors as organism) in explaining TikTok addiction behaviour (as the response). By adopting SOR framework and employing the flow theory as a guide, this study develops a conceptual model of TikTok addiction behaviour. The model posits that users experience with the system leads to TikTok addiction behaviour. This article contributes to our understanding of TikTok addiction among adolescence and suggests possible solutions to curb this prevailing social problem in society. Theoretical and managerial implications are subsequently discussed in light of the conclusion.
Keywords: TikTok , information system , flow , addiction behaviour
With the development of technology, short-form video media has gradually become a new favourite of people, which has dramatically changed the way people communicate and interact ( Ngai et al., 2015 ). TikTok, known as Douyin in China, has 1.5 billion active users so far, and downloaded more than 1 billion times around the world, and is already ahead of competitors like Netflix, YouTube, Snapchat and Facebook ( Omar & Dequan, 2020 ; Weimann & Masri, 2020 ).
TikTok is also the most popular social media platform among millennials in China ( Jung & Zhou, 2019 ). The characteristics of younger group users is short attention span and easily to get immersed in the content they like. According to the features and preferences of this age group, designers build a special algorithm (Feed for You) to customize video content for each user ( Figliola, 2020 ).
When adolescent user exposed with more and more matched content, they will extend the using time, and immersed in TikTok. According to statistics, active TikTok users open the app 8 times per day on average, about 22% of TikTok users use the app for more than an hour a day ( Iqbal, 2020 ). During the COVID-19 outbreak globally, Chinese government has implemented the lockdown. people stay at home without going outside, the average time spent per day by TikTok users rose to 122.3 minutes, that's almost double the 68.8 minutes recorded in 2019, and the number of daily active users increased by 19.4% during the period ( Iqbal, 2020 ) and most of them were active between 9 p.m. and 12 a.m., when about 26.3 percent of its users are online ( Mou, 2020 ).
But excessive immersion leads to users' attachment and addiction to social media ( Cao et al., 2020 ; Weimann & Masri, 2020 ), it then caused depression, anxiety, insomnia, poor eyesight, academic problem, low work performance, etc. ( Beyens et al., 2016 ; Enez Darcin et al., 2016 ; Fu et al., 2020 ; Weinstein & Lejoyeux, 2010 ). These adverse outcomes are well-known indicators of addiction ( Gao et al., 2017 ).
Thus, similar to Facebook addiction, short-form video app addiction may be another sub-category of Internet addiction ( Zhang et al., 2019 ). Some scholars believe that the phenomenon of TikTok addiction is expanding ( Zhang et al., 2019 ).
The existing literature on social media use behaviour, while very commendable, most of the researches only considered single factors such as the technology characteristic ( Rahi et al., 2020 ), website design quality ( Ma et al., 2019 ), perceived usefulness and perceived ease of use ( Ifinedo, 2018 ), IS quality ( Idemudia et al., 2018 ), satisfaction and attitude ( Zhang et al., 2016 ), the user personality ( Omar & Dequan, 2020 ), cognitive factors ( Liao et al., 2009 ), etc.,. In addition, when explaining social media use behaviour, they over-rely on use and gratification theory (U&G), theory of planed behaviour (TPB), technology continuance theory (TCT) and expectation conformation theory (ECM) models and other models. Further research needs to combine both media factors (external factors) and user factors (internal factors) to comprehensively investigate user behaviour.
This research is guided by the following research questions:
RQ1: How do the external factors (information quality and system quality) affect internal factors (flow experience)?
RQ2: Do internal factors (flow experience) have a significant effect on the TikTok addiction behaviour?
RQ3: Do internal factors (flow experience) mediate the effect between external factors and TikTok addiction behaviour?
Purpose of Statement
The current conceptual paper aims to develop theoretical framework by proposing the relationship between information quality, system quality, flow, and addictive behaviour. More importantly, to support theoretical framework, two theories were employed including SOR and flow theory. Because So far, there is no comprehensive effort to integrate the factors that induce users to use social media into a single model. This requires further research to develop a wholistic cause and chain framework to help gain more explanatory power and illuminate social media use behaviour.
Literature Review and propositions development
Current research on the short-form video app TikTok has focused on user adoption ( Omar & Dequan, 2020 ), and the business and social value created (such as job opportunity) ( Hu, 2020 ; Xu et al., 2019 ). Recently, scholars have gradually begun to use SOR to explain user behaviour through the combination of internal and external factors, but most studies focus on its negative consequences, like depression, anxiety, insomnia, poor vision, academic problems, low job performance ( Cao & Sun, 2018 ; Fu et al., 2020 ; Luqman et al., 2017, 2020; Moqbel, 2020 ; Whelan et al., 2020 ).
Despite SOR contributes the body of literature on addiction behaviour, the researchers believe that it still ignores the formation of addictive behaviour. To explore the factors of TikTok addictive behaviour in the emerging short-form video medium, SOR model was adopted in this study, and IS model and flow theory were integrated into SOR to develop a comprehensive theoretical framework and expand our understanding of adolescent TikTok addictive behaviour. Drawing on these theories, the conceptual framework of this paper will be reviewed and propositions.
The SOR model, also known as the environmental psychological model, was developed by Mehrabian and Russell ( 1974 ). In the SOR framework, it is assumed that environmental cues would stimulate individual’s emotional and cognitive state, which leads to certain behavioural ( Lee et al., 2018 ; Mehrabian & Russell, 1974 ).
As a meta-theory to explain human behaviour process, SOR is used to predict the cognitive judgment and subsequent behaviour or intention of network users. The model has been successfully used to explain consumer behaviours, social media applications, virtual experiences, gamification studies ( Cho et al., 2019 ; Kamboj et al., 2018 ; Sun et al., 2018 ; Triantoro et al., 2019 ; Xu et al., 2019 ). It can explain the internal psychological change and interruption response of individuals when they face the environmental stimulus produced by media. For example, both technological environments and virtual psychological experiences have significant effects on the behaviour of social network users ( Luqman et al., 2017 ). Short-form video applications have a lot in common with social media and SNS. However, as an emerging platform, the current research is still in the initial stage, and we could borrow the theoretical framework from social media research.
Stimuli: External factors: IS mode
In the SOR framework, a stimulus refers to "the environment that an individual encounter ( Mehrabian & Russell, 1974 ). In previous studies, the technical aspects of virtual space have been treated as environmental stimuli ( Animesh et al., 2011 ; Zhang et al., 2015 ). A study in the field of information systems (IS) used this framework to explain how information technology attributes relate to the user's internal state and subsequent adoption behaviour ( Benlian, 2015 ). Application quality is divided into information quality and system quality ( Almahamid et al., 2016 ), this site quality structure is a major factor in assessing site users' expectations and perceptions of site quality ( DeLone & McLean, 2003 ; Liang & Chen, 2009 ).
Information quality refers to the accuracy, completeness, and freshness of website content, which IS the user's evaluation of IS's performance in providing information based on their experience in using the system ( McKinney et al., 2002 ). It reflects the relevance, timeliness and adequacy of the information provided by the platform ( Kim et al., 2003 ). This assessment is based on the content of the IS website and needs to be personalized, complete, relevant, and easy to use and provide security aspects to encourage online use ( DeLone & McLean, 2003 ). Recently, the IS success model has been used to understand mobile user behaviour. For example, Gao and Bai ( 2014 ) used the IS model to explain the continuous intention of users of mobile social network services and found that information quality was positively correlated with user experience of using mobile social network services.
System quality refers to the degree to which a website functions, such as accessibility, reliability, and response time ( DeLone & McLean, 2003 ). It represents the technical capability of a website to provide users with simple and quick access to information while ensuring reliability and security ( Teo et al., 2008 ). A well-designed system is necessary to reap organizational benefits, such as reduced costs, improved process efficiency, and increased revenue. Conversely, a poorly designed system may be disruptive to the organization, leading to increased product costs and organizational inefficiency ( Ghasemaghaei & Hassanein, 2015 ; Gorla et al., 2010 ).
Organism: Flow as international factors
The next element of the SOR framework is the organism component, consisting of cognitive and affective mediating states, expressed in the process of regulating the relationship between stimuli and individual responses ( Chen & Chang, 2008 ; Mehrabian & Russell, 1974 ). According to Gao and Bai ( 2014 ), the Organism is a customer's cognitive judgment of the online consumer experience, presented as a stream experience. In this study, we also consider flow as organism component.
SOR model provides a theoretical basis for the mediating effect of flow experience. Studies using SOR framework have shown that consumer internal states (organisms) can play a mediating role between timulus and consumer response behaviour ( Gao & Bai, 2014 ; Ha & Lennon, 2010 ). Computer-mediated communication is a typical situation in which users can experience a psychological state of flow ( Lee et al., 2018 ).
The flow theory was first proposed by Csikszentmihalyi ( 1975 ). It refers to a state of deep immersion in a pleasingly optimal psychological experience ( Novak et al., 2000 ), which is a key driver of persistence ( Chang, 2013 ; Khang et al., 2013 ). Individuals experiencing flow may become so completely lost in the activities they are doing that they lose awareness of time and their surroundings ( Csikszentmihalyi, 1975 ).
Since the short-form video application is an experience product, the user's value mainly comes from their experience during the use process, they can feel great fun. Therefore, based on the SOR model, combined with previous mediation effect on internal cognitive status of research, we have reason to believe that flow in the information system quality and the intermediary role between user reaction, affect the user's internal mental process, which affect their behavioural responses, the flow theory is applied to study the short-form video application addictive behaviour of users.
Response: TikTok addiction behaviour
The response component in the SOR framework refers to the result, final action or reaction, including psychological reaction such as attitude or behaviour, which can be divided into approach behaviour and avoidance behaviour ( Mehrabian & Russell, 1974 ). Approaching behaviour is a positive response and avoiding behaviour is a negative response.
More recently, as algorithmic technology has been upgraded, users have been given more matched content, and using these features has led to varying degrees of immersion, which in turn has induced addictive behaviour. In social media, users are exposed to various technical features or functions, such as user-provided experience, technical stress, exhaustion, and user profiles, which all affect users' participation in social media. As mentioned above, behaviour is closely related to the psychological experience of using social networks ( Zhang et al., 2016 ). TikTok is an emerging short-form video app that offers customized content to users based on their preferences. These fun features and personalized content have addictive entertainment value for very young users. Then, we use addictive behaviour as response to immersive experiences associated with the use of smartphone-based short-form video media.
Information system quality to flow experience.
According to the previous research ( Zhou et al., 2010 ), information quality has a significant impact on users' flow experience, which in turn determines users' loyalty. There are many factors that affect flow, Jung et al. ( 2009 ) point out that content quality affects the flow experience of mobile TV users. Zhou ( 2014 ) confirmed the impact of system quality and information quality on user flow experience of mobile Internet sites as well.
TikTok has its unique way of improving the user experience compared to other social media. For example, offer a variety of special effects filters, fun stickers, and video editing tools to help users spice up their videos. TikTok also offers customized content to users based on their preferences. These fun features and personalized content have addictive entertainment value for very young users. Therefore, this study believes that in order to enable users to have flow experience in TikTok, the positive cognition of the two attributes (information quality and system quality) provided by the platform makes users to immerse themselves in the use process and thus to have flow experience. Therefore, it is very easy for users, especially teenagers who lack self-control, to become addicted to short-form video apps. From this we derive the proposition:
Preposition 1: Information quality has a positive influence on user flow experience.
Preposition 2: System quality has a positive influence on user flow experience.
Flow experience to TikTok addiction
Recent research provides some empirical support that flow can have negative outcomes. For example, Chou and Ting ( 2003 ) found that flow significantly affects online game addiction. Previous studies have shown that smartphone users may experience flow while playing games on their devices ( Joo, 2016 ) and browsing the Internet ( Kim & Han, 2014 ). When people want to have a positive experience of flow, they are also easily addicted to media platforms ( Salehan & Negahban, 2013 ). In this study, we propose that flow may influence the addictive use of short-form video apps in a similar way. We predict that flow may be an important stage prior to users' addictive behaviour ( Khang et al., 2013 ). Therefore, we believe that people who experience flow during the use of short-form video apps are more likely to engage in addictive behaviour. We now raise the proposition:
Preposition 3: Flow experience has a positive influence on TikTok addiction behavioural.
The rest of this paper describes the theoretical background of SOR model, IS theory, flow theory and addictive behaviour in detail. Then, the conceptual framework and three propositions are used to study the influence of the combination of internal and external factors on addictive behaviour.
This paper is a conceptual work that outlines some research prepositions to understand TikTok addition behaviour. This proposed conceptual model should be tested empirically. To statistically validate our conceptual model, users’ perception of information and system qualities as well as their experience with flow and addiction behaviour need to be gauged. Hence, a survey research is the most suitable method to gauge users’ perceptions and behaviours. This is consistent with past research which also used survey method to examine TikTok usage behaviour ( Omar & Dequan, 2020 ). It involves identifying sampling technique, measurement, and data analysis.
With regards to sampling technique, adolescents or young people is the targeted sample. According to Mou ( 2020 ), the largest age group of TikTok users is 6-17 years old, accounting for 31.59%, followed by 18-24 years old (30.14), 25-30 years old (20.85%), 31-35 years old (8.66%), and over 35 years old (8.76). Hence, individuals between the ages of 14 and 18 who have used TikTok in the past year could be an appropriate sample for study. Researchers can adopt non-probability sampling such as virtual snowballing sampling or network sampling to reach out to the TikTok users online.
As for the measurement of constructs, the items can be adopted from past research. Measures for information quality and system quality can be drawn from Kim et al. ( 2003 ) and Zhang et al. ( 2016 ). Meanwhile, the measurement of flow experience can be taken from Zaman et al. ( 2010 ) and Zhang et al. ( 2014 ). Similarly, the TikTok addiction behaviour’s measurement is found in Khang et al. ( 2013 ); Kim et al. ( 2003 ). Table, 1 presents the various constructs in the current framework and strategies for their measurement.
In terms of data analysis, partial least squares (PLS-SEM) analysis is the most option to test the proposed model. This is because PLS-SE< is suitable for the identification of complex critical structural models ( Hair et al., 2019 ). As suggested by Hair et al., ( 2019 ), the study model should be tested in two steps. Firstly, the measurement model should be evaluated to establish the validity and reliability of the questionnaire. Then, in the second step, the structural model should be tested using the bootstrapping technique in order to test the proposed research prepositions.
This study has conceptualized the addictive behaviour of users of short-form video applications, in a word, it is a negative behavioural result of users' flow experience under the influence of external factors of the information system. In this context, this paper proposes a conceptual model that provides theoretical and practical benefits from the perspective of SOR model and flow theory.
Theoretically, this study will contribute to the media literature by confirming the SOR model in the context of TikTok addictive behaviour. It will provide strong evidence that internal factors (flow experience) and external factors (IS quality) can lead to adolescents' addiction to TikTok. This study advances the understanding of adolescent addiction behaviour in TikTok through IS quality and flow factors by applying the modified SOR model ( Belk, 1975 ). Previous literature conducted on social media were heavily rely on theory of planed behaviour (TPB), technology continuance theory (TCT) and expectation conformation theory (ECM) models. These studies analyse the causes of addiction from the perspective of the individual, ignoring external environmental stimuli. Therefore, this study overcomes the defects mentioned before. Based on previous literature, we combine the internal factors (flow) with external factors (information quality and system quality) to revaluate the relationship between information system, flow, and addiction behaviour.
This research also has practical contributions. Social media addiction can have many negative effects on young users and the society, so it is necessary for scholars and practitioners to solve this problem. As for TikTok adolescents’ users, excessive immersion on media platform can actively trigger addictive behaviours, which can lead many problems such as depression and anxiety. Therefore, This could raise legal liability and ethical issues for TikTok operator ( Gong et al., 2020 ; Zheng & Lee, 2016 ). Adolescents are at a critical stage of development, media operators should cultivate user with healthy social media use habits, instead of inducing them to become addicted. Therefore, when facing addiction issue, the operators should help users to get rid of it. For example, adolescent mode can be set to limit the content provided to adolescents and control the duration of their use (excessive use of the reminder or disconnection system), this method has been proven to be an effective way to control addictive behaviours ( Chen et al., 2017 ).
Limitations and future research
Although this article presents diverse theoretical and practical implications, limitations are still existing for future research. First, the conceptual framework and propositions must be tested. Due to the current epidemic, the work and life of TikTok users are in an abnormal state. The life at home leads to the prolonged use of smartphone, which may exaggerate the effect of addiction. Therefore, the proposed theoretical model can be used as a reference for subsequent empirical research. Second, the formation of addiction is a complex problem, only adopt quantitative methods may lead to research bias. Future studies could combine qualitative and quantitative method to gain depth of understanding of short-form video application addiction behaviour.
Almahamid, S. M., Tweiqat, A. F., & Almanaseer, M. S. (2016). University website quality characteristics and success: Lecturers’ perspective. International Journal of Business Information Systems, 22(1), 41–61. DOI:
Animesh, A., Pinsonneault, A., Yang, S. B., & Oh, W. (2011). An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products. MIS Quarterly: Management Information Systems, 35(3), 789–810. DOI:
Belk, R. W. (1975). Situational Variables and Consumer Behavior. Journal of Consumer Research, 2(3), 157. DOI:
Benlian, A. (2015). Web personalization cues and their differential effects on user assessments of website value. Journal of Management Information Systems, 32(1), 225–260. DOI:
Beyens, I., Frison, E., & Eggermont, S. (2016). “I don’t want to miss a thing”: Adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Computers in Human Behavior, 64, 1–8. DOI:
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. DOI:
Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An S-O-R perspective. In Computers in Human Behavior (Vol. 81, pp. 10–18). DOI:
Chang, C. C. (2013). Examining users′ intention to continue using social network games: A flow experience perspective. Telematics and Informatics, 30(4), 311-321.
Chen, C., Zhang, K. Z. K., Gong, X., Zhao, S. J., Lee, M. K. O., & Liang, L. (2017). Understanding compulsive smartphone use: An empirical test of a flow-based model. International Journal of Information Management, 37(5), 438–454. DOI:
Chen, S. W., & Chang, H. H. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review, 32(6), 818–841.
Cho, W. C., Lee, K. Y., & Yang, S. B. (2019). What makes you feel attached to smartwatches? The stimulus–organism–response (S–O–R) perspectives. Information Technology and People, 32(2), 319–343. DOI:
Chou, T. J., & Ting, C. C. (2003). The Role of Flow Experience in Cyber-Game Addiction. Cyberpsychology and Behavior, 6(6), 663–675. DOI:
Csikszentmihalyi, M. (1975). Play and intrinsic rewards. Journal of Humanistic Psychology, 15, 41–63.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30.
Enez Darcin, A., Kose, S., Noyan, C. O., Nurmedov, S., Yılmaz, O., & Dilbaz, N. (2016). Smartphone addiction and its relationship with social anxiety and loneliness. Behaviour and Information Technology, 35(7), 520–525. DOI:
Figliola, P. (2020). TikTok: Technology Overview and Issues (CRS Report Number: R46543).
Fu, S., Chen, X., & Zheng, H. (2020). Exploring an adverse impact of smartphone overuse on academic performance via health issues: a stimulus-organism-response perspective. In Behaviour and Information Technology. DOI:
Gao, L., & Bai, X. (2014). An empirical study on continuance intention of mobile social networking services: integrating the IS success model, network externalities and flow theory. 26(2), 168–189.
Gao, W., Liu, Z., & Li, J. (2017). How does social presence influence SNS addiction? A belongingness theory perspective. Computers in Human Behavior, 77, 347–355. DOI:
Ghasemaghaei, M., & Hassanein, K. (2015). Online information quality and consumer satisfaction: The moderating roles of contextual factors - A meta-analysis. Information and Management, 52(8), 965–981. DOI:
Gong, M., Yu, L., & Luqman, A. (2020). Understanding the formation mechanism of mobile social networking site addiction: evidence from WeChat users. Behaviour and Information Technology, 39(11), 1176–1191. DOI:
Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. Journal of Strategic Information Systems, 19(3), 207–228. DOI:
Ha, Y., & Lennon, S. J. (2010). Online visual merchandising (VMD) cues and consumer pleasure and arousal: Purchasing versus browsing situation. Psychology and Marketing, 27(2), 141–165. DOI:
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. DOI:
Hu, Y. (2020). Research on the commercial value of Tiktok in China. Academic Journal of Business & Management, 2(7), 57–64. DOI:
Idemudia, E. C., Raisinghani, M. S., & Samuel-Ojo, O. (2018). The contributing factors of continuance usage of social media: An empirical analysis. Information Systems Frontiers, 20(6), 1267–1280. DOI:
Ifinedo, P. (2018). Determinants of students’ continuance intention to use blogs to learn: an empirical investigation. Behaviour and Information Technology, 37(4), 381–392. DOI:
Iqbal, M. (2020). TikTok revenue and usage statistics. In Business of Apps. https://www.businessofapps.com/data/tik-tok-statistics/
Joo, J. (2016). Exploring Korean Collegians’ Smartphone Game Behavior: Focusing on Conciseness, Perceived Ease of Use, Perceived Enjoyment, Flow, and Intent to Use. Journal of Digital Convergence, 14(1), 379–386. DOI:
Jung, H., & Zhou, Q. (2019). Learning and Sharing Creative Skills with Short Videos: A Case Study of User Behavior in TikTok and Bilibili. International Association of Societies of Design Research Conference, 10, 25–50. https://www.researchgate.net/publication/335335984
Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123–129. DOI:
Kamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y. (2018). Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus-Organism-Response. International Journal of Information Management, 39, 169–185. DOI:
Khang, H., Kim, J. K., & Kim, Y. (2013). Self-traits and motivations as antecedents of digital media flow and addiction: The Internet, mobile phones, and video games. Computers in Human Behavior, 29(6), 2416–2424. DOI:
Kim, H. W., Xu, Y., Koh, J., Kim, J., Lee, J., & Choi, D. (2003). A Comparison of Online Trust Building Factors between Potential Customers and Repeat Customers. Journal of the Association for Information Systems, 5(6), 392–420.
Kim, Y. J., & Han, J. (2014). Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Computers in human behavior, 33, 256-269.
Lee, C. H., Chiang, H. S., & Hsiao, K. L. (2018). What drives stickiness in location-based AR games? An examination of flow and satisfaction. Telematics and Informatics, 35(7), 1958-1970. DOI:
Liang, C. J., & Chen, H. J. (2009). A study of the impacts of website quality on customer relationship performance. Total Quality Management and Business Excellence, 20(9), 971–988. DOI:
Liao, C., Palvia, P., & Chen, J. L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4), 309–320. DOI:
Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544–555. DOI:
Luqman, A., Masood, A., Weng, Q., Ali, A., & Rasheed, M. I. (2020). Linking Excessive SNS Use, Technological Friction, Strain, and Discontinuance: The Moderating Role of Guilt. Information Systems Management, 37(2), 94–112. DOI:
Ma, Y., Ruangkanjanases, A., & Chen, S. C. (2019). Investigating the impact of critical factors on continuance intention towards cross-border shopping websites. Sustainability (Switzerland), 11(21). DOI:
McKinney, V., Yoon, K., & Zahedi, F. (2002). The measurement of Web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296–315. DOI:
Mehrabian, A., & Russell, J. A. (1974). An Approach to Environmental Psychology. 266.
Moqbel, M. (2020). Understanding the Relationship between Smartphone Addiction and Well-Being: The Mediation of Mindfulnessand Moderation of Hedonic Apps. Proceedings of the 53rd Hawaii International Conference on System Sciences, 3, 6083–6092. DOI:
Mou, J. B. (2020). Study on Social Media Marketing Campaign Strategy-TikTok and Instagram. MIT Sloan School of Management, 3(8), 1–41.
Ngai, E. W. T., Tao, S. S. C., & Moon, K. K. L. (2015). Social media research: Theories, constructs, and conceptual frameworks. International Journal of Information Management, 35(1), 33–44. DOI:
Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42. DOI:
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. DOI:
Rahi, S., Khan, M. M., & Alghizzawi, M. (2020). Extension of technology continuance theory (TCT) with task technology fit (TTF) in the context of Internet banking user continuance intention. International Journal of Quality and Reliability Management. DOI:
Salehan, M., & Negahban, A. (2013). Social networking on smartphones: When mobile phones become addictive. Computers in Human Behavior, 29(6), 2632–2639. DOI:
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data processing and text mining technologies on electronic medical records: A review. Journal of Healthcare Engineering, 2018. DOI:
Teo, T. S. H., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99–132. DOI:
Triantoro, T., Gopal, R., Benbunan-Fich, R., & Lang, G. (2019). Would you like to play? A comparison of a gamified survey with a traditional online survey method. International Journal of Information Management, 49, 242–252. DOI:
Weimann, G., & Masri, N. (2020). Research Note: Spreading Hate on TikTok. In Studies in Conflict and Terrorism. DOI:
Weinstein, A., & Lejoyeux, M. (2010). Internet addiction or excessive internet use. American Journal of Drug and Alcohol Abuse, 36(5), 277–283. DOI:
Whelan, E., Islam, A. K. M. N., & Brooks, S. (2020). Applying the SOBC paradigm to explain how social media overload affects academic performance. Computers and Education, 143(August 2019), 103692. DOI:
Xu, L., Yan, X., & Zhang, Z. (2019). Research on the Causes of the “Tik Tok” App Becoming Popular and the Existing Problems. Journal of Advanced Management Science, 7(2), 59–63. DOI:
Zaman, M., Anandarajan, M., & Dai, Q. (2010). Experiencing flow with instant messaging and its facilitating role on creative behaviors. Computers in Human Behavior, 26(5), 1009–1018. DOI:
Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? the impact of technological environments and virtual customer experiences. Information and Management, 51(8), 1017–1030. DOI:
Zhang, H., Lu, Y., Wang, B., & Wu, S. (2015). The impacts of technological environments and co-creation experiences on customer participation. Information and Management, 52(4), 468–482. DOI:
Zhang, K., Min, Q., Liu, Z., & Liu, Z. (2016). Understanding microblog continuance usage intention: an integrated model. Aslib Journal of Information Management, 68(6), 772–792. DOI:
Zhang, X., Wu, Y., & Liu, S. (2019). Exploring short-form video application addiction: Socio-technical and attachment perspectives. Telematics and Informatics, 42(April), 101243. DOI:
Zheng, X., & Lee, M. K. O. (2016). Excessive use of mobile social networking sites: Negative consequences on individuals. Computers in Human Behavior, 65, 65–76. DOI:
Zhou, T. (2014). Understanding continuance usage intention of mobile internet sites. Universal Access in the Information Society, 13(3), 329–337. DOI:
Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile SNS users’ loyalty. Industrial Management and Data Systems, 110(6), 930–946. DOI:
About this article
31 January 2022
Print ISBN (optional)
Communication, Media, Disruptive Era, Digital Era, Media Technology
Cite this article as:
Yao, Q., & Omar, B. (2022). TikTok Addiction Behaviour Among Users: A Conceptual Model and Research Propositions. In J. A. Wahab, H. Mustafa, & N. Ismail (Eds.), Rethinking Communication and Media Studies in the Disruptive Era, vol 123. European Proceedings of Social and Behavioural Sciences (pp. 231-243). European Publisher. https://doi.org/10.15405/epsbs.2022.01.02.19
We care about your privacy
Manage My Preferences
These cookies are essential in order to enable you to move around the site and use its features, such as accessing secure areas of the site. Without these cookies, services you have asked for cannot be provided.
Third-party advertising and social media cookies are used to (1) deliver advertisements more relevant to you and your interests; (2) limit the number of times you see an advertisement; (3) help measure the effectiveness of the advertising campaign; and (4) understand people’s behavior after they view an advertisement. They remember that you have visited a site and quite often they will be linked to site functionality provided by the other organization. This may impact the content and messages you see on other websites you visit.
- Open supplemental data
- Reference Manager
- Simple TEXT file
People also looked at
Brief research report article, reliability and validity of the problematic tiktok use scale among the general population.
- 1 Department of Child Care and Youth Services, Pamukkale University, Denizli, Türkiye
- 2 Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- 3 Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
Introduction: This study aims to provide a scale for measuring problematic TikTok use levels by adapting items from the Instagram Addiction Scale.
Methods: The 372 participants were determined by a convenience sampling method, and data were collected through Google online forms. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed for construct validity and criterion-related validity analysis. Criterion-related validity for the Problematic TikTok Use Scale (PTTUS) was tested using correlation analysis between the Bergen Social Media Addiction Scale and Social Media Use Disorder Scale.
Results: EFA indicated that a three-factor structure should be formed. The first factor is the sub-dimension of obsession and consists of 4 items, the second factor is the escapism sub-dimension and consists of 6 items, and the third factor is the lack of control sub-dimension and consists of 6 items. The model fit for adapting the PTTUS into Turkish was examined with first-level CFA, χ2/sd, RMSEA, CFI, GFI, AGFI, and SRMR, the obtained values show that the three-factor structure of the scale provides acceptable fit. Reliability analyses showed that Cronbach’s alpha internal consistency reliability coefficient ranged from 0.83 to 0.90; McDonald’s Omega reliability values was 0.84 to 0.90, and test–retest correlation coefficient ranged from 0.68 to 0.73, indicating sufficient internal consistency and test–retest reliability.
Conclusion: Based on this information, PTTUS is a measurement tool with sufficient psychometric properties that can be applied to determine individuals’ levels of problematic TikTok use.
The first smartphones began to enter people’s lives in the early 2000s, offering ease of use without limitations of time and place. They have since become a necessity in many areas of life; smartphone use has become widespread, and its importance has gradually increased. Internet usage on smartphones now exceeds the rate of internet usage on other devices, such as computers and tablets, reaching 95.5%. Social media is the most common type of smartphone internet use. Among social media applications, YouTube (23.7%) is the most popular, followed by Facebook (23.6%) and TikTok (19.6%) in terms of time spent. There has been a significant increase in social media use, especially during the COVID-19 (Coranavirus) Pandemic ( 1 ).
Behavioral addiction is a person’s actions and activities that cause physiological, psychological and social problems and continue to be done uncontrollably despite the person’s desire to quit, thus considering this behavior as unimportant and continuing to do it even if it harms herself/himself and her/him environment. Behavioral addiction according to DSM-5; It has features such as being overly preoccupied with behavior, decreased ability to control behavior, developing tolerance for behavioral, exhibiting excessive negative emotions when trying to avoid behavior, and causing negative psychological problems such as stress and depression.
TikTok was launched in 2016 by the China-based company ByteDance, under the name Musical.ly; it was renamed “TikTok” a year later ( 2 ). The TikTok application is used by downloading it to a smartphone and allows users to record videos of less than 3 min, which users can edit themselves ( 3 ). Some features include adding audio and images, making live broadcasts, and earning a certain amount of income based on users’ number of followers. It differs from other social media platforms in that it can add audio and images to videos, produce content in line with followers’ interest with short videos, enable more interaction, and involve users in an interactive process. TikTok allows people to both entertain and earn income while producing content and trying to attract followers’ attention, which increases its use.
TikTok was downloaded more than 2 billion times in 2021, and most users are adolescents and young adults (16–35 years old) ( 4 ). According to the statistical data, 68.97% of TikTok users are under the age of 24, and 73.69% are under the age of 30 ( 5 ). Social media defines the phenomena it creates with the name of the social media network. For example, someone who is famous on YouTube is called a YouTuber, someone who is famous on Instagram is an Instagrammer, and someone famous on TikTok is defined as TikToker. TikTokers prepare and share content for reasons such as social acceptance, feeling comfortable, and satisfaction ( 6 ), which contributes to increasing TikTok usage.
It has been reported that social media have negative physical, psychological, emotional, and social effects on individuals ( 7 – 10 ). Previous studies have investigated and defined Facebook ( 11 – 13 ), Twitter, and YouTube ( 14 , 15 ) addiction. Social media addiction is accepted as a subtype of internet addiction, which is one of the behavioral addiction types ( 8 , 10 , 12 ). Some recent studies emphasize that many symptoms seen in internet addiction are also included in social media addiction. In addition, in recent years, social media, which has increased its use, can make individuals more addicted. In previous years, there are many studies on social media addictions such as Facebook Addiction, Twitter Addiction, Youtube Addiction, and what these addiction types are has been defined. For example, there is the Social Media Addiction (SMD) scale, which was developed by Tutkun-Ünal ( 16 ) and whose reliability and validity studies were conducted, in order to detect social media addiction. This scale was developed to measure the social media addictions of university students in various ways such as gender, age, class level, applications used, school where they study, social media usage tools, people they live with, and duration of use of social networks. Another social media addiction scale was adapted into Turkish by Demirci ( 17 ). The scale aims to determine the mental pursuit, mood change, tolerance, deprivation, conflict and unsuccessful attempts of individuals to use social media. There are also social media addiction scales developed on different samples ( 18 , 19 ). Scale adaptation studies were also carried out in order to measure Facebook addiction, another social media platform. The “Facebook Addiction Scale” was developed by Kimberly Young in 1998 to measure internet addiction and was adapted to Facebook by Çam ( 20 ) and translated into Turkish. There is also a facebook addiction scale developed by Turkyilmaz ( 13 ) and Akın et al. ( 11 ). As a result, it is seen that a wide variety of scales have been developed or adapted to determine the sub-types of technology or the levels of addiction in various social media platforms. However, it is stated that the TikTok application, which has become widespread in recent years, is now at the level of addiction. In the literature review, no scale was found to measure problematic TikTok use. In this respect, the absence of scale for the problematic TikTok use emerges as an important deficiency in the field.
Today, TikTok, one of the social media platforms today, has become an problematic due to its increasing usage rate. Today, problematic TikTok use has also become a concern, and can be defined as spending excessive time on one’s own page to increase the number of followers; increasing the amount of time spent using the application day by day; the inability to control the time spent; and eventually getting bored of real life and coming to seeing one’s virtual identity as real and arranging one’s lifestyle accordingly. Moreover, individuals who are addicted may feel tense, restless, stressed, and lonely when they cannot use it.
India has the highest number of TikTok users, followed by the United States and Turkey. The average monthly TikTok usage time in Turkey is 18.8 h ( 1 ). Since problematic TikTok use is a relatively new phenomenon, there is comparatively little research investigating it, and it can be difficult to determine problematic TikTok use. While there are scales measuring other types of social media addiction in the literature, there is no scale for specifically measuring problematic TikTok use. Thus, this study aims to provide a scale for measuring problematic TikTok use.
Before starting reliability and validity studies of the new scale, the necessary permissions were obtained from the author of the scale via email. And then the development process of the scale, necessary permissions were obtained from Pamukkale University Social and Human Sciences Ethics Committee in accordance with the decision of Document Date: 31.05.2022 and Number of Documents: E-93803232-622.02-211,894. After the items of the original scale were adapted for the PTTUS, they were sent to three experts working on the subject for content validity. In line with the suggestions from each expert, the item list of the scale was finalized and a pilot application was made. This indicated that the items of the scale worked well, and the trial phase began. Data were obtained via Google forms.
Participants and procedures
Participants were 500 Turkish adults who were determined by convenience sampling. Data were collected using Google forms. Forms were distributed over the internet to Pamukkale University students, their relatives, and university staff. Responses with missing or extreme data were excluded from the analysis, leaving a final sample of 372 (74.4%). The construct validity of the scale was examined by confirmatory analysis. For factor analysis, according to Tabachnick and Fidell ( 21 ), 300 people in the research group is considered good, and 500 people is considered very good. In this respect, it can be said that the student group in which the studies are carried out is sufficient in terms of the number of personnel required by statistical analysis. The first page of the form included the purpose of the study, the ages and genders of the researchers, and the voluntary participant consent form. The second page of the form contained the scale items, which were scored using Likert-type scales. Participants knew about and used TikTok, and came from 52 cities in 7 regions of Turkey.
All rating scales were delivered to the participants and all data were collected within 2 weeks. In addition, the scale was sent to the same participants after a two-week break for test–retest reliability. Participants were asked to add their email addresses before submitting their questionnaires (used for informational purposes for the study only, and the information was deleted immediately after the analysis) for use in the test–retest analysis. A total of 215 participants; therefore, the test–retest was carried out with data from 215 participants.
The original form of the Problematic TikTok Use Scale
The PTTUS was developed by adapting D’Souza et al. ( 22 ) Instagram Addiction Scale, whose psychometric properties were determined by Kavaklı and İnan ( 23 ) ( Supplementary Material S1 ). Permission was obtained from both the authors of the scale and the social and human sciences ethics committee of the university before adapting the scale. After the items of the Instagram Addiction Scale were adapted for the PTTUS, expert opinion was sought for the scale items and suggested corrections were made. The original Instagram Addiction Scale consisted of 21 items, 16 of which were adapted for the PTTUS. Originally, a 21-item PTTUS was presented to participants. However, as a result of EFA, five items whose factor load was not sufficient to be included in any factor were removed from the scale. Items were scored using a 5-point Likert-type scale (1 = never and 5 = always ). One sample item is “I often upload videos to TikTok.” Higher scores indicate higher levels of problematic TikTok use. In the current study, the Cronbach’s Alpha reliability of the scale was 0.90; the Cronbach’s Alpha reliability of the sub-dimensions; 0.84 for obsession sub-dimension; 0.90 for escapism sub-dimension and 0.85 for lack of control sub-dimension. In the current study, the McDonald’s Omega reliability of the scale was 0.90; the McDonald’s Omega reliability of the sub-dimensions; 0.84 for obsession sub-dimension; 0.90 for escapism sub-dimension and 0.85 for lack of control sub-dimension.
Bergen Social Media Addiction scale
The BSMAS scale was developed by Schou Andreassen et al. ( 24 ) and adapted into Turkish by Demirci ( 17 ). The scale consists of six items measuring mental exertion, mood change, tolerance, withdrawal, conflict, and unsuccessful attempts to quit. Items are scored using a 5-point Likert-type scale; higher scores reflect higher dependence on social network sites. Total scores range from 6 to 30. In the adaptation study, the Cronbach alpha internal consistency reliability coefficient of the scale was found 0.83. In this current research, the Cronbach alpha internal consistency reliability coefficient of the scale was found 0.857, and McDonald’s Omega reliability value was calculated as 0.860.
Social Media Disorder scale
Developed by Van den Eijnden et al. ( 25 ) to measure individuals’ social media addiction levels, the SMD scale was adapted into Turkish by Sarıçam and Adam-Karduz ( 26 ) using a nine-item form. Each item measures a different sub-dimension (occupation, endurance, deprivation, insistence, escape, problems, deception, displacement, conflict). In the present study, the internal consistency coefficient of the scale was 0.75 and Cronbach’s alpha reliability coefficient was 0.82. In the adaptation study, the Cronbach alpha internal consistency reliability coefficient of the scale was found 0.75. In this current research, the Cronbach alpha internal consistency reliability coefficient of this scale was found 0.879, and McDonald’s Omega reliability value was calculated as 0.883.
We examined the construct validity and reliability of the Turkish version of the PTTUS. Normality assumption was tested based on skewness and kurtosis of each item. To check the sampling adequacy and data suitability, the Kaiser–Meyer–Olkin (KMO) value and Bartlett’s test of sphericity were checked. The Exploratory Factor Analysis (EFA) with Varimax technique was used to determine the factor structure of the PTTUS. Factors with an eigenvalue above 1 were defined as acceptable. Confirmatory Factor Analysis (CFA) with the diagonally weighted least squares method was carried to check the factor structure of the PTTUS, and a satisfactory model fit for the model was defined by a standardized root-mean square residual (SRMR) value ≤0.05, root-mean-square-error of approximation (RMSEA) value ≤0.10, and comparative fit index (CFI) and Tucker–Lewis index (TLI) values ≥0.90. The Cronbach’s alpha method was preferred in the reliability analysis of the scale. The BSMAS and SMD scale were used for criterion-related validity. The receiver operating characteristic (ROC) analysis was performed to explore the appropriate cut-off score of the PTTUS on accordance with addiction (excessive or problematic use of social media and spending at least 8.5 to 21.5 h a week online). Validity and reliability analyses were conducted using the SPSS 22 and AMOS 20 package programs.
All 500 participants who knew about and used TikTok from 52 cities in 7 regions of Turkey responded to the survey. A total of 201 (54.04%) participants were female, and the age range was 18–40 (x̄ = 24.35; sd: 2.3).
Analysis of exploratory factors
In the EFA, Kaiser-Meyer-Olkin (KMO) and Bartlett’s Sphericity tests were performed to test the suitability of the obtained data for factor analysis. Considering the analysis results, the KMO was 0.87 and the Approximate Chi-Square ( χ 2 ) result was 2918.35 ( p < 0.001). Since the KMO was higher than 0.60 and the Barlett’s Sphericity test was significant, the dataset in the research group was considered to be suitable for factor analysis. ( 27 ) Findings related to the EFA were tested in the validity analysis. Varimax technique was used to determine the factor structure of the scale, so the factor number of the scale, the load values of each item, and the correlation of the items with the whole scale (Item-total correlation) were determined. Factors with an eigenvalue above 1 were accepted as the basis in the analysis ( 21 ).
In the adapted scale, a 3-dimensional structure with the sub-dimensions of obsession, escapism, and lack of control was obtained. Items 1–4 are in the first sub-dimension, items 5–10 are scored in the second sub-dimension, and items 11–16 are scored in the third sub-dimension. The eigenvalues and explanatory variances of the factor structures obtained as a result of the EFA are given in Table 1 .
Table 1 . Ratios of variance explained by eigenvalues obtained as a result of exploratory factor analysis.
As a result of the EFA, the obtained three-factor structure explained 62.99% of the total variance. When the explanation rates of the factors were examined, factor 1 explained 24.23% of the total variance, factor 2 explained 21.84%, and factor 3 explained 16.92%.
Analysis of confirmatory factors
CFA was performed to determine the scale’s structure ( 28 , 29 ). Fit indices frequently used in CFA include Chi-square fit (χ2) and ratio of Chi-square to degrees of freedom (χ2/sd), Root Mean Square Errors of Approximation (RMSEA), Adjusted Goodness-of-Fit Index (AGFI), Comparative Fit Index (CFI), Goodness-of-Fit Index (GFI), and Standardized Root-Square Means (SRMR) ( 30 ). The data of the CFA performed to determine the construct validity of the PTTUS are presented in Table 2 and Figure 1 . Item-total correlation and Cronbach’s alpha if item deleted reliability coefficient for each item are also shown ( Table 2 ).
Table 2 . Item factor loads for the Prpblematic TikTok Use Scale.
Figure 1 . Path diagram and factor loads of the Problematic TikTok Use Scale.
Looking at the scale’s sub-dimensions, factor 1 is the obsession sub-dimension comprising items 1–4; factor 2 is the escapism sub-dimension comprising items 5–10; and factor 3 is the lack of control sub-dimension comprising items 11–16. The item factor load distributions for the overall scale are shown in Table 2 . To determine the item validity of the PTTUS, the item-total correlation results were examined. It is seen that the item-total correlation values vary between 0.40 and 0.70. Considering that items with an item-total correlation value of 0.30 and above are considered sufficient in terms of distinguishing the quality to be measured ( 27 ), all the items in the scale are sufficiently related to the scale’s total score and the scale item validity is ensured.
Confirmatory factor analysis
While adapting PTTUS, model fit was examined using first-level CFA. The fit index values of the PTTUS were calculated χ2 = 1036.86, p < 0.01, χ 2 /sd = 4.19, RMSEA = 0.10, CFI = 0.88, GFI = 0.86, AGFI = 0.81, SRMR = 0.09 as before covariance. After was made covariance the fit indices of the PTTUS seem to be sufficient [CFA: χ2 = 1036.86, p < 0.01, χ 2 /sd = 4.14, RMSEA = 0.08, CFI = 0.95, GFI = 0.89, AGFI = 0.85, SRMR = 0.08]. Considering the statistical values of fit, a value of χ2/sd below 5 indicates acceptable fit, a RMSEA value between 0.00 and 0.05 indicates good fit, and a value between 0.05 and 0.08 indicates acceptable fit ( 31 , 32 ). The factor loads of the items in the scale range between 0.44 and 0.93. The analysis of the first-level CFA is presented in Table 3 .
Table 3 . Model fit indices for the Problematic TikTok Use Scale.
For the criterion-related validity, correlations between the BSMAS and SMD scale were calculated and the analysis results are given in Supplementary Table S1 .
When the relationships between PTTUS and BSMAS and SMD scale were examined, the following positive and significant relationships were found: between the PTTUS total score and BSMAS ( r = 0.56, p < 0.01) and SMD scale ( r = 0.49, p < 0.01); between the obsession sub-dimension and BSMAS ( r = 0.35, p < 0.01) and SMD scale ( r = 0.30, p < 0.01); between the escapism sub-dimension and BSMAS ( r = 0.52, p < 0.01) and SMD scale ( r = 0.39, p < 0.01); between the lack of control dimension and BSMAS ( r = 0.45, p < 0.01) and SMD scale ( r = 0.49, p < 0.01). Considering the analysis results align with the theoretical framework, it can be said that the PTTUS has criterion-related validity.
In this study, Cronbach’s alpha internal consistency coefficient and test–retest reliability analysis were performed at two-week intervals to determine the reliability of the scale; the findings are presented in Supplementary Table S2 .
Cronbach’s alpha internal consistency coefficient and test–retest reliability analysis were used to determine the reliability of the PTTUS. The analysis showed Cronbach’s alpha reliability coefficient of the total scale was calculated as 0.90. Reliability was 0.83, 0.90, and 0.85 for the obsession, escapism, and lack of control sub-dimensions, respectively. McDonald’s Omega reliability coefficient of the total scale was calculated as 0.90. Reliability for the obsession sub-dimension of the scale was 0.84; for the escapism sub-dimension, the reliability was 0.90; for the lack of control sub-dimension, the reliability was calculated as 0.85. The test–retest reliability analysis coefficients were 0.73 for the total scale, 0.68 for obsession, 0.68 for escapism, and 0.70 for lack of control. Considering that reliability coefficients of 0.70 and above are considered reliable in the scale adaptation process ( 33 ), it can be said that the internal consistency and test–retest reliability coefficients of the PTTUS are sufficient.
The receiver operating characteristic
We performed the analysis of ROC and the area under the curve (AUC) for determining the PTTUS. Supplementary Table S3 shows the analysis of ROC with the parameters required. The cut-off value obtained for the PTTUS was ≥31.5, with sensitivity and specificity percentages of 88.9 and 37.5%, respectively. Subjects are diagnosed as experiencing problematic TikTok use if their score is ≥32.
The current study aimed to develop the PTTUS. For this purpose, the items of the Instagram Addiction Scale were adapted for Problematic TikTok Use. The TikTok application is closer to the Instagram application than the Facebook application due to its intended use and the developable content it allows. However, it is said that Instagram and TikTok applications tend to be used mostly on smartphones. In addition, it is seen that the use of Facebook application tends towards a more restricted age group. In addition, it has been determined that individuals use more interactive and instant sharing applications on social media ( 34 ). However, as the items of the Instagram addiction scale were evaluated to be more useful for measuring problematic TikTok use, it was deemed appropriate to use the items of the Instagram Addiction Scale. The Instagram addiction scale is a more inclusive scale since the number of items is 16. At the same time, the Instagram addiction scale was preferred because it is a more up-to-date scale. Language validity and content validity were performed for the obtained scale. In addition, EFA and CFA were performed for construct validity and criterion-related validity analysis was calculated. Cronbach’s alpha internal consistency, McDonald’s Omega value and test–retest coefficients were performed at two-week intervals to test the scale’s reliability.
The language validity of the scale was ensured in line with expert opinions obtained during the adaptation of the original scale items to TikTok. Before the EFA and CFA analysis, the dataset’s suitability for factor analysis was tested using KMO and Barlett tests. The dataset was considered suitable for factor analysis if the KMO was higher than 0.60 and the Barlett Sphericity test was significant. ( 27 , 35 ) EFA and CFA showed that a three-factor structure consisting of 16 items explained 62.99% of the total variance, and the structure of the scale was confirmed. It can be said that PTTUS has a sufficient total variance explanation rate.
The CFA indicated that a three-factor structure is formed. The first factor comprises the obsession sub-dimension and consists of four items; the second factor is the escapism sub-dimension and consists of six items, and the third factor is the lack of control sub-dimension and consists of six items. The item factor load distributions of the scale showed load values ranging between 0.43 and 0.93. Considering that the item factor load value should be >0.32 ( 21 ) and if an item is included in more than one factor, there should be a difference of at least 0.10 in the item load between the factors ( 36 ), it can be said that the item factor load values of the three-factor PTTUS are sufficient. The model fit for adapting the PTTUS into Turkish was examined with the first-level CFA. χ2/sd, RMSEA, CFI, GFI, AGFI and SRMR values obtained as a result show that the three-factor structure of the scale provides acceptable fit.
In the study, correlations between BSMAS and SMD scale were calculated for criterion-related validity. The results showed that the total score and sub-dimensions of PTTUS had significant relationships with BSMAS and SMD scale. Considering the analysis results and the theoretical framework of PTTUS, it can be said that PTTUS has criterion-related validity. Corrected item-total correlations ranged from 0.40 to 0.70. The Cronbach’s alpha internal consistency and test–retest analyses were used to determine the scale’s reliability. Cronbach’s alpha internal consistency reliability coefficient ranged from 0.83 to 0.90, and test–retest correlation coefficient ranged from 0.68 to 0.73; it can be said that the internal consistency and test–retest reliability of the PTTUS are sufficient.
In recent years, internet usage has become widespread in Turkey and worldwide, and social media is one of the most concentrated areas of use. Social media allows people to interact with each other and share their opinions, thoughts, photos, and videos through applications such as YouTube, Facebook, Instagram, and TikTok. However, dependence on these applications can cause individual and interpersonal problems. The literature indicates that as addiction to social media applications increases, negative mood disorders such as anxiety, neuroticism, and depression and the time spent using social media increase ( 37 – 43 ). Some studies have shown that the relationship between addiction to social media applications and anxiety symptoms is higher than the relationship between symptoms of depression and anxiety ( 24 , 25 ). It can be said that TikTok, which is mostly used by adolescents and young adults (16–35 years old), is likely to cause negative effects. Therefore, it is necessary to have a scale for measuring problematic TikTok use. In addition, it is recommended to develop interventions to prevent and reduce problematic TikTok use so that individuals experience less anxiety, depression, and other negative effects. Based on this information, PTTUS is a measurement tool with sufficient psychometric properties that can be applied to determine individuals’ levels of problematic use. The scale measures three sub-dimensions, and both the total score and the scores of the sub-dimensions can be obtained. Higher scores obtained from the scale indicate higher levels of problematic use.
The study was conducted online using a university population. Online data collection is being used increasingly, especially in social science. So large amounts of data were accessed quickly and at a low cost ( 44 ). Online data collection is as valid and reliable as traditional data collection methods. In addition to this strength, this study has several limitations. First, the participants were recruited using convenience sampling, so a more representative sample of the population is needed to generalize the findings. Second, data were obtained through self-report, so it may not be free from social desirability and recall biases. Future studies are needed to validate the PTTUS using an objective rating method rather than self-report. Third, the study was conducted in a non-clinical population. Further research is needed in clinical samples. Fourth, because this study only included participants in Turkey, there was no comparison of the PTTUS results between Western and Eastern countries, which could have important implications for healthcare professionals.
Nevertheless, this study provides initial support for using PTTUS as a reliable and valid measure of problematic TikTok use in Turkish adolescents and young adults. This easy-to-use scale has good psychometric properties and allows mental health professionals to screen for problematic TikTok use. It may also be useful for other researchers conducting studies related to problematic TikTok use in Turkey. Future studies are needed to demonstrate the usefulness of the scale for various age groups and problematic TikTok use behaviors in Türkiye.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving human participants were reviewed and approved by Ethics Committee: Pamukkale University, Social and Human Sciences Research and Publication Ethics Committee Document Decision Date and Number: 31.05.2022-E.211894. The patients/participants provided their written informed consent to participate in this study.
AG, TO, and SC: conceptualization. AG and TO: data curation, formal analysis, and visualization. AG, TO, SY, and SC: methodology. All authors, writing—original draft and review and editing. All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1068431/full#supplementary-material
1.WEARESOCIAL. Digital. Another year of bumper growth. Available online at: https://wearesocial.com/uk/blog/2022/01/digital-2022-another-year-of-bumper-growth-2/ (2022).
2.Xiong, Y, and Ji, Y. From content platform to relationship platform: analysis of the attribute change of Tiktok short video (citation has been translated from Chinese7 language). View Publish . (2019) 4:29–34. doi: 10.54097/ehss.v5i.2901
CrossRef Full Text | Google Scholar
3.Wang, Y. Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent, an example of TikTok (DouYin). Comput Hum Behav . (2020) 110:106373. doi: 10.1016/j.chb.2020.106373
4.Montag, C, Yang, H, and Elhai, JD. On the psychology of TikTok use: a first glimpse from empirical findings. Front. Public Health . (2021) 9:641673. doi: 10.3389/fpubh.2021.641673
PubMed Abstract | CrossRef Full Text | Google Scholar
5.Yang, S, Zhao, Y, and Ma, Y. Analysis of the reasons and development of short video application -taking TikTok as an example In:. 2019 9th international conference on information and social science (ICISS 2019) . Francis Academic Press, UK (2019). 340–3. doi: 10.25236/iciss.2019.062
6.Lodice, R, and Papapicco, C. To be a TikToker in COVID-19 era: an experience of social influence. Online J Comm Media Technol . (2021) 11:1–12. doi: 10.30935/ojcmt/9615
7.Kawada, T. Comment on "smartphone addiction proneness is associated with subjective-objective sleep discrepancy in patients with insomnia disorder". Psychiatry Investig . (2022) 19:595–6. doi: 10.30773/pi.2022.0024
8.Kim, SS, and Bae, SM. Social anxiety and social networking service addiction proneness in university students: the mediating effects of experiential avoidance and interpersonal problems. Psychiatry Investig . (2022) 19:462–9. doi: 10.30773/pi.2021.0298
9.Lim, YJ. Exploratory structural equation modeling analysis of the social network site use motives scale. Psychiatry Investig . (2022) 19:146–53. doi: 10.30773/pi.2021.0092
10.Shin, NY. Psychometric properties of the Bergen social media addiction scale in Korean Young adults. Psychiatry Investig . (2022) 19:356–61. doi: 10.30773/pi.2021.0294
11.Akin, A, Demirci, I, and Kara, S. The validity and reliability of Turkish version of the Facebook addiction scale. Academic perspective international refereed journal of. Soc Sci . (2017) 59:65–72.
12.Kuss, DJ, and Griffiths, MD. Online social networking and addiction--a review of the psychological literature. Int J Environ Res Public Health . (2011) 8:3528–52. doi: 10.3390/ijerph8093528
13.Turkyilmaz, M. The translation of Facebook addiction scale into Turkish and impact of Facebook addition to reading ability. J Acad Soc Sci Stud . (2015) 6:265–80. doi: 10.9761/JASSS2942
14.Kircaburun, K. Effects of gender and personality differences on Twitter addiction among Turkish undergraduates. J Educ Pract . (2016) 6:265–42. doi: 10.9761/JASSS2942
15.Moghavvemi, SA, Binti Sulaiman, A, I, JN, and Kasem, N. Facebook and YouTube addiction: the usage pattern of Malaysian students In:. 2017 international conference on research and innovation in information systems (ICRIIS) . Langkawi, Malaysia (2017). 1–6. doi: 10.1109/ICRIIS.2017.8002516
16.Tutgun-Ünal, A. Social media addiction: A study on university students . İstanbul: Marmara University, Institute of Social Sciences (2015).
17.Demirci, I. The adaptation of the Bergen social media addiction scale to Turkish and its evaluation of relationships with depression and anxiety symptoms. Anatolian J Psychiatry . (2019) 20:1–22. doi: 10.5455/apd.41585
18.Orbatu, D, Eliaçık, K, Alagayut, D, Hortu, H, Demirçelik, Y, Bolat, N, et al. Development of adolescent social media addiction scale: study of validity and reliability. Anatolian J Psychiatry . (202) 1:56–61. doi: 10.5455/apd.77273
19.Özgenel, M, Canpolat, Ö, and Ekşi, H. Social media addiction scale for adolescents: validity and reliability study. Addicta . (2019) 0:629–62. doi: 10.15805/addicta.2019.6.3.0086
20.Çam, E. Educational and general purpose facebook uses and Facebook addictions of teacher candidates (SAU education faculty example) . Sakarya: Sakarya University Institute of Educational Sciences (2012).
21.Tabachnick, BG, and Fidell, LS. Using multivariate statistics . New York: Allyn and Bacon (2007).
22.D’Souza, L, Samyukta, A, and Bivera, TJ. Development and validation of test for Instagram addiction (TIA). Int J Ind Psychol . (2018) 6:4–14. doi: 10.25215/0603.81
23.Kavaklı, M, and İnan, E. Psychometric properties and correlates of the Turkish version of Instagram addiction scale (IAS). J Clin Psychol Res . (2021) 5:86–97. doi: 10.5455/kpd.26024438m000037
24.Schou Andreassen, C, Billieux, J, Griffiths, MD, Kuss, DJ, Demetrovics, Z, Mazzoni, E, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav . (2016) 30:252–62. doi: 10.1037/adb0000160
25.van den Eijnden, RJJM, Lemmens, JS, and Valkenburg, PM. The social media disorder scale. Comput Hum Behav . (2016) 61:478–87. doi: 10.1016/j.chb.2016.03.038
26.Sarıçam, H, and Karduz, FFA. The adaptation of the social media disorder scale to Turkish culture: validity and reliability study. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi . (2018) 9:117–36. doi: 10.21031/epod.335607
27.Filed, A. Discovering statistics using SPSS . London: SAGE Publications Ltd. (2009).
28.Çokluk, Ö, Şekercioğlu, G, and Büyüköztürk, Ş. Multivariate statistics for social sciences: SPSS and LISREL applications . Ankara: Pegem Academy Publishing (2014).
29.Seçer, İ. Practical data analysis with SPSS and LISREL . Ankara: Anı Publishing (2015).
30.Brown, TA. Confirmatory factor analysis for applied research . New York, US: Guilford Press (2006).
31.Kline, RB. Principles and practice of structural equation modeling . New York: The Guilford Press (1998).
32.Schermelleh-Engel, K, Moosbrugger, H, and Müller, H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. Methods of psychological research. Online . (2003) 8:23–74.
33.Robinson, JP, Shaver, PR, and Wrightsman, LS. Criteria for scale selection and evaluation in measure of personality and social psychological attitudes . San Diego: California Academic Press (1991).
34.Kuss, D, and Griffiths, M. Social networking sites and addiction: ten lessons learned. Int J Environ Res Public Health . (2017) 14:311–28. doi: 10.3390/ijerph14030311
35.Pallant, J. SPSS survival manual: A step by step guide to data analysis using SPSS for windows . Australia: Australian Copyright (2005).
36.Büyüköztürk, S. Data analysis handbook for social sciences . Ankara: Pegem Akademi Publishing (2008).
37.Andreassen, CS, Torsheim, T, Brunborg, GS, and Pallesen, S. Development of a Facebook addiction scale. Psychol Rep . (2012) 110:501–17. doi: 10.2466/02.09.18.PR0.110.2.501-517
38.Koc, M, and Gulyagci, S. Facebook addiction among Turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychol Behav Soc Netw . (2013) 16:297–84. doi: 10.1089/cyber.2012.0249
39.Lin, CY, Brostrom, A, Nilsen, P, Griffiths, MD, and Pakpour, AH. Psychometric validation of the Persian Bergen social media addiction scale using classic test theory and Rasch models. J Behav Addict . (2017) 6:620–9. doi: 10.1556/2006.6.2017.071
40.Lin, CY, Ganji, M, Pontes, HM, Imani, V, Brostrom, A, Griffiths, MD, et al. Psychometric evaluation of the Persian internet disorder scale among adolescents. J Behav Addict . (2018) 7:665–75. doi: 10.1556/2006.7.2018.88
41.Pantic, I, Damjanovic, A, Todorovic, J, Topalovic, D, Bojovic-Jovic, D, Ristic, S, et al. Association between online social networking and depression in high school students: behavioral physiology viewpoint. Psychiatr Danub . (2012) 24:90–3.
42.Uygur, OF, Uygur, H, Chung, S, Ahmed, O, Demiroz, D, Aydın, EF, et al. Validity and reliability of the Turkish version of the Glasgow sleep effort scale. Sleep Med . (2022) 98:144–51. doi: 10.1016/j.sleep.2022.06.022
43.Yang, SC, and Tung, CJ. Comparison of internet addicts and non-addicts in Taiwanese high school. Comp Human Behav . (2007) 23:79–96. doi: 10.1016/j.chb.2004.03.037
44.Young, KS. Cognitive behavior therapy with ınternet addicts: treatment outcomes and implications. CyberPsychol Behav . (2007) 10:671–9. doi: 10.1089/cpb.2007.9971
Keywords: validation, reliability, social media, psychology, TikTok, problematic use
Citation: Günlü A, Oral T, Yoo S and Chung S (2023) Reliability and validity of the problematic TikTok Use Scale among the general population. Front. Psychiatry . 14:1068431. doi: 10.3389/fpsyt.2023.1068431
Received: 12 October 2022; Accepted: 22 February 2023; Published: 28 March 2023.
Copyright © 2023 Günlü, Oral, Yoo and Chung. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
This article is part of the Research Topic
The Impact of Social Media, Gaming, and Smartphone Usage on Mental Health
On the Psychology of TikTok Use: A First Glimpse From Empirical Findings
- 1 Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany.
- 2 The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
- 3 Faculty of Psychology, Tianjin Normal University, Academy of Psychology and Behavior, Tianjin, China.
- 4 Department of Psychology, University of Toledo, Toledo, OH, United States.
- 5 Department of Psychiatry, University of Toledo, Toledo, OH, United States.
- PMID: 33816425
- PMCID: PMC8010681
- DOI: 10.3389/fpubh.2021.641673
Keywords: DouYin; TikTok; musical.ly; personality; problematic social media use; social media; social media addiction; uses and gratification.
Copyright © 2021 Montag, Yang and Elhai.
- Social Media*
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.
- Account settings
- Advanced Search
- Journal List
- Int J Environ Res Public Health
Research on Adolescents Regarding the Indirect Effect of Depression, Anxiety, and Stress between TikTok Use Disorder and Memory Loss
The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.
This research involved the participation of 3036 Chinese students in the first and second years of senior high school. The adolescents were active users of TikTok. The mediating effect of depression, anxiety, and stress between TikTok use disorder and memory loss was investigated. A forward and backward digit span test was applied to measure memory loss. Structural equation modeling (SEM) was established, and SPSS Amos was used for analysis. The results show a partial mediation effect of depression and anxiety between TikTok use disorder and forward digit span. A partial mediation effect of depression, anxiety, and stress between TikTok use disorder and backward digit span is also shown. These results also show gender differences. Attention should be given to male students, who have more depression, anxiety, and stress than female students; they also have more memory loss.
1.1. internet, smartphone, or tiktok use disorder.
The concept of “non-chemical addiction” was introduced in 1990 [ 1 ]. At that time, engaging in excessive online activities such as online sex and Internet games was initially called Internet addiction [ 2 ]. In 1990, only about 250 behavioral addiction papers were published, while in 2013, 2563 papers were published. General information, social networking, email, chat, videos, and films were reported to be the most popular online activities of Internet users [ 3 ]. Internet use disorder has a strongly negative influence on normal psychological development, and it can lead to syndromes such as stress [ 4 ]. Some researchers have investigated Internet communication in terms of the use of social networking sites such as WhatsApp and Facebook [ 5 , 6 ]. Easy access to the Internet with smartphones increased the popularity of social networks [ 7 ]. Very early research on the addictive use of the Internet was published in 1996 [ 8 ]. Many researchers prefer to use the term “Internet use disorder” [ 5 , 9 ] or “smartphone use disorder” [ 10 , 11 , 12 ], instead of “addiction”; however, these terms have still not been accepted by ICD-11 or DSM-5 [ 13 ].
TikTok is the most popular smartphone application of Chinese origin in the world. The total number of active TikTok users worldwide is 1.5 billion, and most of them are teenagers [ 14 ]. According to a report at the end of 2020, the number of monthly active TikTok users worldwide was 800 million. About 81% of Chinese users were young people under 35 years old [ 15 ]. The gratification of entertainment is the main driver for TikTok users. Adolescent groups are more active on the application because of their identity-creation and social contact needs [ 16 ]. Some researchers have indicated that self-expression and use satisfaction are associated with the motivations of TikTok users [ 17 ].
1.2. Internet or Smartphone Use Disorder and Depression, Anxiety, and Stress
According to a report by the WHO [ 18 ], the prevalence of depression and anxiety was 4.4% and 3.6%, respectively. Generally, females have a higher prevalence of depression and anxiety than males. In adolescence, the prevalence of depression and anxiety reaches the highest point [ 19 ]. Junior high school students, because of the high academic pressure and their monotonous life, are vulnerable to mental health problems such as depression [ 20 ].
Depression and social anxiety in junior high school students can be used as a mediator to explain the relationship between Internet use disorder and maladaptive cognition [ 21 ]. In research on adolescents [ 22 ], stress was highly associated with social anxiety. Social anxiety can act as a mediator between Internet use disorder and stress. Research on junior college students with an average age of 17 years [ 23 ] investigated the relationship between problematic Internet use, depression, anxiety, and stress. The more problematic the Internet use, the heavier the depression, anxiety, or stress. Moreover, depression, anxiety, and stress were positively associated with each other.
The relationship between anxiety, depression, and smartphone use disorder has been researched [ 24 ]. Depression and anxiety were shown to be highly correlated, and a positive correlation was found between anxiety and smartphone use disorder, but not between depression and smartphone use disorder. Some studies [ 25 , 26 , 27 , 28 , 29 ] showed that the use of social networking sites was positively linked to depression. However, other studies [ 30 ] showed that there was no correlation between the use of social networking sites and depression. Between users and non-users of Facebook, no differences were found with regard to depression, anxiety, and stress [ 31 ]. Internet use expectancies and dysfunctional cognition, such as suppression, maladaptive problem-solving, and avoidance, can be regarded as mediators between Internet use disorder and psychopathological aspects such as depression and social anxiety [ 32 ].
1.3. Depression, Anxiety, Stress, and Working Memory Capacity
The influence of depression on memory has been researched [ 33 ]. In this research, the influence of depression on memory varied by age and gender. The relationship between anxiety and working memory capacity, as an element of fluid cognition, has been researched [ 34 ]. The causal pathways from anxiety to low working memory were established. Furthermore, low working memory was found to have an effect on cognitive vulnerability, which has a feedback effect on anxiety. The relationship between anxiety, working memory, and gender has been researched [ 35 ]. State anxiety was found to vary by gender. However, a gender effect on trait anxiety was not found. Visual working memory was positively linked to math anxiety. However, there was no significant correlation between visual working memory and state anxiety or trait anxiety. A positive correlation between anxiety and stress was found [ 36 ]. Both anxiety and stress were negatively linked to visuospatial working memory, but they were not linked to verbal working memory, although there was a strong correlation between visuospatial and verbal working memory. The relationship between stress and working memory capacity has been researched [ 37 ]. Stress was found to be negatively linked to working memory. However, a correlation between state anxiety and working memory was not found. Some researchers [ 38 ] found a correlation between depression, anxiety, and working memory capacity, but found that situational stress had no influence on working memory capacity.
A correlation between forward and backward digit span was reported. Difficulties with forward and backward digit span in children were linked to learning disorders [ 39 ]. Brener [ 40 ] conducted an experimental investigation of memory span, including a list of materials with increasing difficulty. Digit span had a lower difficulty level than consonants, colors, and words. The influence of depression on reading span and word span has been researched [ 41 ]. A lower capacity of reading and word span by depressed patients was shown, and reading span was shorter than word span for both depressed and non-depressed persons. Digit span as a subtest of WAIS-IV was used to search its relationship with depression and anxiety. However, no significant correlation was found in this study [ 42 ]. Some researchers [ 43 ] did not find any correlations between forward or backward digit span and state anxiety, trait anxiety, or stress.
1.4. Internet or Smartphone Use Disorder and Working Memory Capacity
Correlations between use disorder, smartphone use disorder, and working memory have been researched [ 44 ]. Smartphone use disorder was found to be highly linked with Internet use disorder. Both Internet and smartphone use disorder were found to be negatively linked with working memory. The correlation between smartphone use disorder and working memory was found to be stronger than the correlation between Internet use disorder and working memory. However, this research did not find a gender effect on Internet or smartphone use disorder and working memory capacity. The mediating effect of depression, anxiety, and stress between problematic social media use and memory capacity was researched [ 45 ]. In this research, the Memory Awareness Rating Scale (MARS-MPS) was used to evaluate memory performance. The PROCESS macro in SPSS was used to analyze the pathways. This study was based on adults with an average age of about 30, and there were 466 valid participants. The results showed that only anxiety had a partial mediating effect between problematic social media use and memory performance.
In this present study, the hypotheses are as follows ( Figure 1 ):
Structural model of hypotheses.
TikTok use disorder (TTUD) is positively linked to memory loss.
TTUD is positively linked to depression, anxiety, and stress.
Depression, anxiety, and stress are positively linked to memory loss.
Depression, anxiety, and stress have a mediating effect between TTUD and memory loss.
2. Materials and Methods
The participants in the study were 3036 Chinese students in the first and second year of senior high school. Their participation was voluntary and anonymous ( Table 1 ).
2.2. Measurement Instruments
The Smartphone Addiction Scale, Short Version (SAS-SV) [ 46 ] was used in this study. TTUD was adapted from the SAS-SV, in which “smartphone” was changed to “TikTok”. This questionnaire consists of 10 items, rated on a 6-point Likert-type scale, ranging from “strongly disagree” coded as 1, to “strongly agree” coded as 6. Higher scores indicate a higher risk of TikTok use disorder. In this study, the Cronbach’s alpha coefficient of TTUD was 0.91.
The Depression Anxiety Stress Scales 21 (DASS-21) [ 47 , 48 ] was used in this study. This questionnaire consists of 21 items rated on 4-point Likert scale, from 0 for “did not apply to me at all” to 3 for “applied to me very much”. Groups of 7 items are used to measure depression, anxiety, and stress. Higher scores indicate more severe symptoms. Cronbach’s alpha of depression was 0.88, anxiety was 0.86, and stress was 0.87.
Forward and backward digit spans were tested to measure memory loss. A number was given at random, beginning with a 2-digit number. The digits were increased until a wrong answer was given. A number was not repeated until a 10-digit number was reached; for example, 112 or 121, in which 1 was repeated, would not happen. The result was regarded as valid for the forward digit span test when the answer was a 3-digit to 11-digit number, and for the backward digit span test when the answer was a 2-digit to a 9-digit number.
2.3. Statistic Software
For this study, SPSS Amos 24.0 and SPSS 26.0 (IBM: New York, NY, USA) were used.
Descriptive information divided by gender is shown in Table 2 .
Descriptive statistics divided by gender.
TTUD, TikTok use disorder; DSF, forward digit span; DSB, backward digit span.
Pearson’s correlations between the research variables are shown in Table 3 .
Pearson’s correlations between research variables.
** p < 0.01.
The results of pathway analysis are shown directly in Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 . First, the results of forward and backward digit span tests of all participants, male and female, were calculated ( Figure 2 and Figure 3 ). Second, the tests of male participants were calculated ( Figure 4 and Figure 5 ). Finally, the tests of female participants were calculated ( Figure 6 and Figure 7 ). Pathway coefficients shown in the figures are unstandardized.
Structural model of forward digit span test for male and female participants. TTUD, TikTok use disorder; DSF, forward digit span. *** p < 0.001.
Structural model of backward digit span test for male and female participants. DSB, backward digit span. ** p < 0.01, *** p < 0.001.
Structural model of forward digit span test for male participants. ** p < 0.01, *** p < 0.001.
Structural model of backward digit span test for male participants. ** p < 0.01, *** p < 0.001.
Structural model of forward digit span test for female participants. ** p < 0.01, *** p < 0.001.
Structural model of backward digit span test for female participants. * p < 0.05, *** p < 0.001.
For all participants, male and female, based on the forward digit span test, depression and anxiety have a partial mediating effect between TTUD and forward digit span memory capacity. Based on the backward digit span test, depression, anxiety, and stress have a partial mediating effect between TTUD and backward digit span memory capacity. For male participants, based on both types of digit span tests, only depression and anxiety showed partial mediating effects. For female participants, the partial mediating effects were the same as for all participants.
The criteria for good model fit were as follows: Chi-square/df < 5, RMSEA < 0.08, CFI > 0.95, and TLI > 0.95 [ 49 ]. Model fit information of the structural models in this study is shown in Table 4 .
Model fit information.
This study was aimed at examining the mediating effect of depression, anxiety, and stress between TTUD and memory loss, focused on adolescents and divided by gender. TTUD was higher for female participants than male participants. This was the same as in early research on smartphone use disorder [ 12 ]. TTUD is positively linked with memory loss (H1). The greater the memory loss, the smaller the digit span. This result is gender independent. In this study, male participants showed more depression, anxiety, and stress than female participants. Kessler [ 50 ] indicated that throughout the lifespan, the prevalence of depression and anxiety in women is 1.5 times higher than in men. Taking a closer look at the gender effect on these symptoms, it did not differ between these results. This study was cross-sectional; prevalence throughout the lifespan was totally independent.
Some researchers have indicated that the gender effect could vary by factors such as age [ 51 ], anamnesis [ 52 ], or other mediating effects [ 53 ]. Gao [ 54 ] investigated college students and found no significant difference in depression or stress in first-year students and no significant difference in depression, anxiety, or stress in third-year students of different genders. TTUD is positively linked to depression, anxiety, and stress (H2). This result agrees with those of other researchers [ 23 ]. Depression, anxiety, and stress are positively linked to memory loss (H3). However, this hypothesis was not proven with regard to stress on the forward digit span test; it was proven with the backward digit span test, except for stress on male participants. Depression, anxiety, and stress have a mediating effect between TTUD and memory loss (H4). The partial mediating effect of depression and anxiety was proven with the forward digit span test, and stress had no mediating effect. However, with the backward digit span test, all three symptoms had a partial mediating effect, except for stress on male participants.
This research investigated a homogeneous group of participants from a normal senior high school in China. This sample was not representative of all adolescents. Generalizing the results will depend on further research. However, attention should be given to male students at senior high schools in China. Although their TTUD scores were lower than those of female students, they suffered more depression, anxiety and stress and had more memory loss than female students. For further studies, longitudinal research would be interesting. Because of the limitation of the cross-sectional design, a conclusion could not be made, as the more severe memory loss in male students resulted from the additional depression, anxiety, and stress they suffered. Furthermore, due to the cross-sectional study design, causal relationships between research variables could not be determined. To determine causal relationships, more information or more research would be needed. Some researchers [ 6 ] regarded depression, anxiety, and stress as independent variables and Internet use disorder as the dependent variable. The effects within or causal relationships between these variables may be reciprocal.
TikTok use disorder (TTUD) is positively linked to memory loss, and it is also positively linked to depression, anxiety, and stress. Depression, anxiety, and stress are positively linked to memory loss. Furthermore, depression, anxiety, and stress have a mediating effect between TTUD and memory loss.
A partial mediation effect of depression and anxiety between TTUD and forward digit span is shown. A partial mediation effect of depression, anxiety, and stress between TTUD and backward digit span is also shown. These results also show gender differences. Attention should be given to male students, who have more depression, anxiety, and stress than female students; they also have more memory loss.
Conceptualization, analysis, investigation, writing, P.S.; supervision, X.D. All authors have read and agreed to the published version of the manuscript.
This research was funded by China Postdoctoral Science Foundation, special funded project: 2019T120799.
Institutional Review Board Statement
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethics committee of School of Journalism and Communication, Southwest University (Project identification code: 2019-01-21-sha).
Informed Consent Statement
Informed consent was received from all the authors and participants before research and publication.
Data Availability Statement
Conflicts of interest.
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- Share full article
If Your Child Is Addicted to TikTok, This May Be the Cure
Children are suffering under the weight of social media. New York lawmakers believe they have a strategy to halt the damage.
By Ginia Bellafante
Ginia Bellafante writes the Big City column, a weekly commentary on the politics, culture and life of New York City.
Over the past few years, hundreds of families and school districts around the country have sued big tech companies on the grounds that the hypnotic properties of social media popular with children have left too many of them unwell. Citing the promotion of the “corpse bride” diet, for example, and other practices around dangerous forms of weight loss, Seattle Public Schools filed a complaint in January arguing that platforms like TikTok and Snapchat have been so unsparing in their delivery of harmful material to students that the resulting anxiety, depression and suicidal ideation were interfering with the school system’s primary mission of educating children.
More recently, in October, the New York attorney general, Letitia James, along with top prosecutors from more than 30 other states, filed suit against Meta alleging that the company put features in place on Instagram and Facebook to intentionally addict children for profit.
Tech companies, claiming First Amendment protections, have sought to get these sorts of suits quickly dismissed. But on Tuesday, a federal judge in California issued a ruling to make that more difficult. In it, she argued that what concerned plaintiffs most — ineffective parental controls, the challenges of deleting accounts, poor age verification and the timing and clustering of notifications to ramp up habitual use — was not the equivalent of speech, so the suits under her review should be allowed to proceed .
Forty years ago, drunken driving was an epidemic, claiming the lives of young people, a seemingly unmanageable problem until a group of mothers committed themselves to pushing for laws that brought accountability. It was a pivotal moment in the modern history of public health, and, in the same way, 2023 is likely to be remembered as an inflection point in the health crisis surrounding social media.
In May came Surgeon General Vivek Murthey’s advisory — a “call for urgent action” — to develop policy around a practice that was eroding adolescent sociality and self-esteem, compromising sleep and sound body image. Both state and federal legislatures have tried to enact laws that would keep certain kinds of emotionally disruptive content out of vision.
If nothing else, these efforts have emerged as a space of détente in our otherwise forever culture wars; TikTok seems to ignite adult rage no matter where you stand on gender-neutral bathrooms or banning “Antiracist Baby.” A Senate bill introduced in the spring — the Protecting Kids on Social Media Act — which would require companies to verify the age of their users was sponsored by unlikely comrades, Chris Murphy, the Democrat from Connecticut, and the Arkansas Republican, Tom Cotton.
The problem with some of the proposed legislation is a focus on prohibition, which leaves interpretations of harm to the discretion of judges and regulators and, in turn, creates an open door to endless litigation. Montana provides the clearest case. In May the governor signed a law banning TikTok outright with the promise of imposing corporate fines if the app was found to be operating in the state. Immediately, both the platform’s parent company, ByteDance, based in China, and TikTok users themselves sued, maintaining that the law was unconstitutional.
New York has chosen to pursue a different path. State lawmakers, hoping to circumvent some of these obstacles and serve as a model for the rest of the country, have bound themselves to an emphasis on distribution rather than content, technical operation over matters of speech. Sponsored by Andrew Gounardes, a state senator from Brooklyn, two bills aim to implement several changes. First, they would require social media companies to restrict the use of predictive algorithmic features meant to keep users on a particular platform longer; second they would allow parents to block access to social media sites between midnight and 6 a.m.
The legislation comes with the very vocal support of Gov. Kathy Hochul and Ms. James. “We want all these social media apps to show kids only the content they want to see,” Mr. Gounardes told me. “If a parent decides otherwise, they can turn the algorithm on. But the default is that it would have to be off.”
Zephyr Teachout, the legal scholar who helped draft the legislation, saw precedent in the way that gambling is regulated. The algorithmic targeting is similar to the kind deployed by slot machines, which over and over supply the tantalizing lineup of oranges and cherries that just keep you pulling the lever, with the elusive jackpot in mind. Any form of online gambling, in fact, as Ms. Teachout pointed out, “involves the algorithmically determined type of content to be delivered, and in most states gambling is prohibited by those under 18.”
Were the law to come under Supreme Court review, a 2011 case in which it struck down a California law banning the sale or rental of violent video games to minors would probably emerge as a reference point. In that instance, even justices who agreed with the majority opinion pointed out that technology changed at high-speed pace, and different circumstances might require a more nuanced approach later on. “They put down a marker that is very relevant to this moment,” Ms. Teachout said. “They said that the court should not simply apply old standards to new and quickly evolving modes of digital media.”
The New York law has been constructed narrowly enough in its creators’ view that courts ought to recognize it as a critical response to a pervasive problem in which we all have a special responsibility. Facebook would operate as it did in its early iteration, when what you received in your feed was only what you had signed up to see. No one would be prevented from looking up whatever they wanted. “It’s just that you couldn’t open up a Taylor Swift page and five clicks later be shown a video of how to harm yourself,” Mr. Gounardes said.
Ginia Bellafante has served as a reporter, critic and, since 2011, as the Big City columnist . She began her career at The Times as a fashion critic, and has also been a television critic. She previously worked at Time magazine. More about Ginia Bellafante
Politics in the New York Region
Dismissed Charges: A Republican councilwoman in Brooklyn who was arrested for carrying a pistol to a pro-Palestinian rally has had charges dismissed against her after it was found that the weapon was unloaded and inoperable.
Montaukett Indian Nation: The Native American tribe from Long Island has fought for years for formal state recognition and won over the State Legislature. But persuading Gov. Kathy Hochul to grant the official status is proving harder to accomplish .
Clean Slate: Roughly two million people convicted of crimes in New York may be eligible to have their records sealed as part of a broad criminal justice initiative that was signed into law on Nov. 16 by the governor.
George Santos: The House Ethics Committee introduced a resolution to expel the Republican congressman from Congress , citing the committee’s damning new report documenting violations of House rules and evidence of pervasive campaign fraud.
A Far-Reaching Decision: The fight over one of the most consequential congressional battlegrounds in the nation has taken center stage in a staid courtroom in Buffalo , as New York Democrats try to redraw the state’s district lines once again ahead of the 2024 election.
- Australia edition
- International edition
- Europe edition
TikTok slammed for being too addictive in app's first 'I quit' essay
It was the second most downloaded app of 2019 but in 2020 has acquired its first ‘why I deleted my TikTok’ essay
P erhaps you’ve just barely wrapped your head around the existence and popularity of TikTok – something about dances? A young woman called Charli D’Amelio ? Does the word “ renegade ” have something to do with it? Sigh.
While you’re sorting that out, at least one young user is already over 2019’s second-most downloaded app. Last Sunday, Cornell University sophomore Niko Nguyen published an essay in the Cornell Daily Sun student paper detailing his personal decision to quit TikTok. According to the Verge’s technology journalist Casey Newton, Nguyen’s post is “the first known ‘why I deleted my TikTok’ essay. An important rite of passage for any social app.”
Nguyen’s rationale includes the fact that the app is an addictive time-suck – certainly a valid concern. The company has also been accused of shadier practices than wasting your evening, including non-consensually harvesting user data and suppressing content made by queer, differently abled and fat creators. The app is said to be under confidential national security review in the US .
The oft-parodied essayistic subgenre of “why-I-quit” stories is most closely associated with writers moving away from New York City – pieces by people who’ve had it up to here with something or other and need to go off. In recent years, a seemingly unending stream of regular social media users and occasionally influencers, like Instagram’s self-proclaimed reality-checker Essena O’Neill , have made a point of departing platforms with dramatic flourish, rather than simply going gentle into the rest of their lives .
Quitting social media holds a particular, virtue-signaling appeal that’s hard to resist; we all know someone who seems to relish the act of declaring their intention to take a hiatus from Twitter or Instagram, whether or not they ultimately succeed. And really, there are plenty of good reasons to disconnect – not least of which being to “ heal your idiot brain” .
Of course, the fact that one person saw it fit to declare TikTok cancelled does not a sea change make – after all, the app was downloaded 1.5bn times as of last November. Nonetheless, there’s always the chance that Nguyen’s story may be a harbinger of what could come: a wave of Gen Z-er’s, sick and tired of reflexively bursting into jerky choreography whenever Doja Cat comes on, and ready to stop mumbling lines from the Broadway production of Beetlejuice in their sleep.
Until then, at least we’ll be entertained .
- Social media
To revist this article, visit My Profile, then View saved stories .
- Artificial Intelligence
- Wired Insider
Ozempic Could Also Help You Drink Less Alcohol
Ozempic and Wegovy’s usefulness might not stop at weight loss. For more than a decade, research has emerged that similar drugs used to treat diabetes have a surprising side-effect: They make people want to drink less alcohol—way less.
The apparent effectiveness of drugs like semaglutide won’t come as a surprise to doctors who have been prescribing these drugs to patients for years. In 2011, researchers in India found that a drug called liraglutide, a GLP-1 receptor used to treat diabetes, significantly reduced alcohol intake in a small group of patients. In fact, nine of the 63 participants surveyed had stopped drinking altogether.
Hints that these kinds of drugs could be used in the treatment of alcoholism go back even further. The first GLP-1 receptor agonist came on the market back in 2005 in the form of exenatide, and the accompanying waning thirst for alcohol has been reported anecdotally over and over ever since. “Some of [the patients] wanted to have a break going on holidays, because they wanted to be able to enjoy a glass of red wine,” says Mette Kruse Klausen, a postdoctoral researcher at the Psychiatric Centre Copenhagen in Denmark. If it were the case that this already-approved, safe drug could stem alcohol cravings, its potential to treat alcohol use disorder, or AUD—estimated to afflict over 280 million people worldwide—was tantalizing.
Follow-up research was slow. First, researchers had to test the application out on animals, which they did—and it did show promise in using GLP-1 receptor agonists for reducing alcohol intake.
Research in humans followed: A randomized clinical trial in Denmark led by Kruse Klausen started in 2017 looking at treating patients with AUD with exenatide. It worked with 127 patients with AUD; half got exenatide, half a placebo. A setback followed: Researchers found that exenatide did not lead to a reduction in the number of heavy-drinking days between the two groups.
Researchers working on the study theorized that the lack of efficacy could have been due to the cognitive behavioral therapy offered to both groups. Another factor could have been that severity of the patients’ AUD was lower than that of other trials looking at treatments for AUD—and research on interventions for people with AUD is just difficult to do, says Kruse Klausen, due to the high dropout rate. Another analysis of the data found that the drug was effective at significantly reducing alcohol intake–but only in the participants who qualified as obese.
Another reason for the trial’s failure could be that exenatide is much less potent than its newer cousin semaglutide, better known as Ozempic. Now that Ozempic is everywhere, anecdotal evidence is mounting that these drugs reduce cravings not just for food, but for online shopping, smoking, nail-biting , and alcohol.
Now, the first empirical evidence to support the idea that drugs like Ozempic could be an effective treatment for AUD is beginning to appear. This week, a new paper published in the Journal of Clinical Psychiatry strengthened the case. The paper relayed a series of case studies: six patients who had been prescribed semaglutide for weight loss, but who also qualified for having AUD. All six of the participants displayed significantly reduced symptoms of AUD—even those who had achieved minimal weight loss.
This small study is only the beginning. The authors are also running a clinical trial in Tulsa, Oklahoma, looking at semaglutide to treat AUD; a sister study is being conducted in Baltimore, Maryland. It’ll be at least a year and a half before those trials have publishable data, so this case series was done in order to set the table for the clinical trial data, say study authors Kyle Simmons, professor of pharmacology and physiology at Oklahoma State University, and Jesse Richards, assistant professor of medicine at the University of Oklahoma. (Richards receives payment from Novo Nordisk and Eli Lilly, who make GLP-1 receptor agonist drugs, to speak at conferences.)
While scientists aren’t certain how these drugs work to dampen alcohol cravings, it’s suspected to work on the same pathways that produce a shrunken appetite. A thirst for booze is thought to be driven by the rewarding properties that alcohol produces, delivered by a bump of dopamine released in the brain. Over time, that dopamine flurry reinforces a want for alcohol.
GLP-1 receptors are found dotted around the body, including in the brain structures that control our reward pathways. These receptors control the release of the hormone GLP-1, which has a multitude of roles to play in the body, including how we respond to alcohol.
What drugs like semaglutide, which mimic the actions of GLP-1, seem to do is lower the amount of the substance required—like food or alcohol—to feel satiated. Richards says some patients report going to an event where they’d normally expect to drink a lot, like a sports game or a fishing trip, “and instead of drinking their normal amount, they would drink one drink, and then kind of get bored and forget about it,” he says.
To understand what’s happening at a neurological level, new clinical trials will not only track alcohol consumption, but also look at how the participants’ brains respond to alcohol cues in an fMRI scanner.
And alcohol is only one of many addictive substances. Researchers are also considering whether drugs like semaglutide could help smoking cessation or treat other types of drug addiction. Alcohol is a good starting point, says Simmons, because there’s a massive patient cohort who tend to suffer from other conditions, like mental illness.
But this new case series study was tiny, and gold-standard clinical trials take time. On November 24, Simmons and Kruse Klausen, among others leading research on this application, penned an editorial for Nature Medicine warning that while their research was showing tantalizing promise, it was far too early to promote the treatment. First, they say, researchers need to collect good-quality clinical trial data.
In the meantime, they emphasize that there are validated effective treatments available for people struggling with alcohol—approved drugs like naltrexone, disulfiram, and acamprosate. But uptake numbers and success rates for long-term abstaining are paltry—in the US, less than 2 percent of patients use available medications for AUD. “We don’t want patients going to their health care provider and saying, ‘Give me some semaglutide because I want to drink less,’” says Simmons.
In a way, that might already be happening. A huge number of people are using semaglutide for diabetes and obesity. Some of those people might also, almost by accident, find it helps with problematic drinking. If that happens, Simmons argues, Ozempic and similar drugs could quickly become the most widely used addiction treatment ever.
You Might Also Like …
📧 Find the best bargains on quality gear with our Deals newsletter
Twitter’s former head of trust and safety team finally breaks her silence
Insiders say Eat Just is in big financial trouble
Bumble, Grindr, and Hinge moderators struggle to keep users—and themselves—safe
The real reason EV repairs are so expensive
Gen Z is leaving dating apps behind
🌞 See if you take a shine to our picks for the best sunglasses and sun protection
Tara C. Smith