the impact of social media on self esteem essay

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Social Media Use and Adolescents’ Self-Esteem: Heading for a Person-Specific Media Effects Paradigm

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Patti Valkenburg, Ine Beyens, J Loes Pouwels, Irene I van Driel, Loes Keijsers, Social Media Use and Adolescents’ Self-Esteem: Heading for a Person-Specific Media Effects Paradigm, Journal of Communication , Volume 71, Issue 1, February 2021, Pages 56–78, https://doi.org/10.1093/joc/jqaa039

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Eighteen earlier studies have investigated the associations between social media use (SMU) and adolescents’ self-esteem, finding weak effects and inconsistent results. A viable hypothesis for these mixed findings is that the effect of SMU differs from adolescent to adolescent. To test this hypothesis, we conducted a preregistered three-week experience sampling study among 387 adolescents (13–15 years, 54% girls). Each adolescent reported on his/her SMU and self-esteem six times per day (126 assessments per participant; 34,930 in total). Using a person-specific, N = 1 method of analysis (Dynamic Structural Equation Modeling), we found that the majority of adolescents (88%) experienced no or very small effects of SMU on self-esteem (−.10 < β < .10), whereas 4% experienced positive (.10 ≤ β ≤ .17) and 8% negative effects (−.21 ≤ β ≤ −.10). Our results suggest that person-specific effects can no longer be ignored in future media effects theories and research.

An important developmental task that adolescents need to accomplish is to acquire self-esteem, the positive and relative stable evaluation of the self. Adolescents’ self-esteem is an important predictor of a healthy peer attachment ( Gorrese & Ruggieri, 2013 ), psychological well-being ( Kernis, 2005 ), and success later in life ( Orth & Robins, 2014 ). In the past decade, a growing number of studies have investigated how adolescents’ social media use (SMU) may affect their self-esteem. Adolescents typically spend 2–3 hours per day on social media to interact with their peers and exchange feedback on their messages and postings ( Valkenburg & Piotrowski, 2017 ). Peer interaction and feedback on the self, both bedrock features of social media, are important predictors of adolescent self-esteem ( Harter, 2012 ). Therefore, understanding the effects of SMU on adolescents’ self-esteem is both important and opportune.

To our knowledge, 18 earlier studies have tried to assess the relationship between SMU and adolescents’ general self-esteem (e.g., Woods & Scott, 2016 ) or their domain-specific self-esteem (e.g., social self-concept; Blomfield Neira & Barber, 2014 ; Košir et al., 2016 ; Valkenburg et al., 2006 ). The ages of the adolescents included in these studies ranged from eight to 19 years. Fifteen of these studies are cross-sectional correlational (e.g., Cingel & Olsen, 2018 ; Meeus et al., 2019 ), two are longitudinal ( Boers et al., 2019 ; Valkenburg et al., 2017 ), and one is experimental ( Thomaes et al., 2010 ). Some of these studies have reported positive effects of SMU on self-esteem (e.g., Blomfield Neira & Barber, 2014 ), others have yielded negative effects (e.g., Woods & Scott, 2016 ), and yet others have found null effects (e.g., Košir et al., 2016 ). It is no wonder that the two meta-analyses on the relationship of SMU and self-esteem have identified their pooled relationships as “close to 0” ( Huang, 2017 , p. 351), “puzzling,” and “complicated” ( Liu & Baumeister, 2016 , p. 85).

While this earlier work has yielded important insights, it leaves two important gaps that may explain these weak effects and inconsistent results. A first gap involves the time frame in which SMU and self-esteem have been assessed in previous studies. Inherent to their design, the cross-correlational studies have measured SMU and self-esteem concurrently, at a single point in time. The two longitudinal studies have assessed both variables at three or four times, with one-year lags, with the aim to establish the potential longer-term effects of SMU on self-esteem ( Boers et al., 2019 ; Valkenburg et al., 2017 ). However, both developmental (e.g., Harter, 2012 ) and self-esteem theories (e.g., Rosenberg, 1986 ) argue that, in addition to such longer-term effects, adolescents’ self-esteem can fluctuate on a daily or even hourly basis as a result of their positive or negative experiences. These theories consider the momentary effects of SMU on self-esteem as the building blocks of its longer-term effects. Investigating such momentary effects of SMU on adolescents’ self-esteem is the first aim of this study.

A second gap in the literature that may explain the weak and inconsistent results in earlier work is that individual differences in susceptibility to the effects of SMU on self-esteem have hardly been taken into account. Studies that did investigate such differences have mostly focused on gender as a moderating variable, without finding any effect ( Kelly et al., 2018 ; Košir et al., 2016 ; Meeus et al., 2019 ; Rodgers et al., 2020 ). However, these null findings may be due to the high variance in susceptibility to the effects of SMU within both the boy and girl groups. After all, if differential susceptibility leads to positive effects among some girls and boys and to negative effects among others, the moderating effect of gender at the aggregate level would be close to zero. Therefore, the time is ripe to investigate differential susceptibility to the effects of SMU at the more fine-grained level of the individual rather than by including group-level moderators. Such an investigation would not only benefit media effects theories (e.g., Valkenburg & Peter, 2013 ), but also self-esteem theories that emphasize that the effects of environmental influences may differ from person to person (e.g., Harter & Whitesell, 2003 ). Investigating such person-specific susceptibility to the effects of SMU is, therefore, the second aim of this study.

To investigate the momentary effects of SMU on self-esteem (first aim), and to assess heterogeneity in these effects (second aim), we employed an experience sampling (ESM) study among 387 middle adolescents (13–15 years), whom we surveyed six times a day for three weeks (126 measurements per person). We measured SMU by asking adolescents on each measurement moment how much time in the past hour they had spent on the three most popular social media platforms among Dutch adolescents ( van Driel et al., 2019 ): Instagram, WhatsApp, and Snapchat. We focused on middle adolescence because this is the period of most significant fluctuations in self-esteem ( Harter, 2012 ). By employing a novel, person-specific method to analyze our intensive longitudinal data, we were able, for the first time, to assess the effects of SMU at the level of the individual adolescent, and to assess how these effects differ from adolescent to adolescent.

Social Media Use and Self-Esteem Level

Personality and social psychological research into the antecedents, consequences, and development of self-esteem has mostly focused on two aspects of self-esteem: self-esteem level and self-esteem instability. Most of this research has focused on self-esteem level, that is, whether it is high or low ( Crocker & Brummelman, 2018 ). This also holds for studies into the effects of SMU. For example, all of the 15 correlational studies have investigated whether adolescents who spend more time with social media report a lower (or higher) level of self-esteem compared to their peers who spend less time with social media (e.g., Apaolaza et al., 2013 , 12–17 years; Barthorpe et al., 2020 , 13–15 years; Bourke, 2013 , 12–16 years; Cingel & Olsen, 2018 , 12–18 years; Kelly et al., 2018 , 14 years; Morin-Major et al., 2016 , 12–17 years; O'Dea & Campbell, 2011 , M age 14; Rodgers et al., 2020 , M age 12.8; Thorisdottir et al., 2019 , 14–16 years; Valkenburg et al., 2006 , 10–19 years; van Eldik et al., 2019 , 9–13 years). In statistical terms, these studies have investigated the between -person relationship of SMU and self-esteem.

The majority of studies into the between-person relationship of SMU and self-esteem used Rosenberg’s (1965) self-esteem scale, which is the most commonly used survey measure to assess general, trait-like levels of self-esteem. These studies asked adolescents at one point in time to evaluate their selves in general or across a certain period in the past (e.g., in the past year). In the current study, we also investigated the between-person relationship between SMU and adolescents’ general levels of self-esteem. But unlike earlier studies, we assessed their levels of SMU and self-esteem by averaging the 126 momentary assessments of both variables across a three-week period. Such in situ assessments generally produce data with greater ecological validity because they are made in the natural flow of daily life, which reduces recall bias ( van Roekel et al., 2019 ). Given the inconsistent results in previous studies, the literature does not allow us to formulate a hypothesis on the between-person association between SMU and self-esteem level. Therefore, we investigated the following research question:

(RQ1) Do adolescents who spend more time with social media report a lower or higher level of self-esteem compared to adolescents who spend less time with social media?

Social Media Use and Self-Esteem Fluctuations

A second strand of personality and social psychological research has focused on the instability of self-esteem. Self-esteem instability refers to the extent to which self-esteem fluctuates within persons ( Kernis, 2005 ). Whereas research into the level of self-esteem has predominantly tried to establish differences in self-esteem between persons, work on self-esteem instability has focused on fluctuations in self-esteem within persons. Rosenberg (1986) distinguishes between two types of within-person self-esteem fluctuations: baseline and barometric instability. Baseline instability refers to potential within-person changes in levels of self-esteem that occur slowly and over an extended period of time. It has been shown, for example, that self-esteem decreases in early adolescence after which it may slowly and steadily increase again in later adolescence ( Harter & Whitesell, 2003 ). Barometric fluctuations, in contrast, reflect short-term within-person fluctuations in self-esteem as a result of one’s everyday positive and negative experiences. Rosenberg (1986) argued that such barometric fluctuations are particularly evident during adolescence, when adolescents typically experience enhanced uncertainty about their identity (i.e., how to define who they are and will become), intimacy (i.e., how to form and maintain meaningful relationships), and sexuality (e.g., how to cope with sexual desire and define their sexual orientation; Steinberg, 2011 ).

One of the aims of the current study is to investigate how SMU may induce within-person fluctuations in barometric self-esteem. Two earlier social media effects studies have focused on within-person effects, one longitudinal study ( Boers et al., 2019 , M age 17.7) and one experiment ( Thomaes et al., 2010 , 8–12 years). Using Rosenberg’s self-esteem scale, Boers et al. found negative within-person effects of SMU on baseline self-esteem. However, because the assessments of SMU and self-esteem were one year apart, and because short-term fluctuations can hardly be derived from designs with longer-term measurement intervals ( Keijsers & van Roekel, 2018 ), this study, although important, may not inform a hypothesis on the influences of SMU on barometric self-esteem.

A within-person experiment by Thomaes et al. (2010) does confirm self-esteem instability theories in the context of SMU. Thomaes et al. based their experiment on Leary and Baumeister’s (2000) Sociometer theory. Like Rosenberg’s theory of self-esteem, Sociometer theory proposes that self-esteem serves as a sociometer (cf. barometer) that gauges the degree of approval and disapproval from one’s social environment. An important proposition of Sociometer theory is that self-esteem changes are accompanied by changes in affect (mood and emotions). Self-esteem (and affect) goes up when people succeed or when others accept them, and it drops when people fail or when others reject them. The results of Thomaes et al. confirmed Sociometer theory: When preadolescents’ online social media profiles were approved by others, their self-esteem increased, and when their online profiles were disapproved, their self-esteem dropped.

In Thomaes et al.’s study, peer approval was experimentally manipulated so that one group of preadolescents (8-13 years) received positive feedback and an equally sized group received negative feedback on their online profiles. In reality, however, peer approval and disapproval in social media interactions are typically not as neatly balanced. In fact, studies have often reported a positivity bias in social media-based interactions (e.g., Reinecke & Trepte, 2014 ; Waterloo et al., 2017 ), meaning that social media users tend to share and receive more positive than negative information. This positivity bias also strongly holds for adolescent social media users. For example, among a national sample of adolescents, only 8% “sometimes” received negative feedback on their posts, whereas 91% “never” or “almost never” received such feedback ( Koutamanis et al., 2015 ). Therefore, on the basis of Sociometer theory, the positivity bias of social media interactions, and the findings of Thomaes et al., we expect an overall positive within-person effect of time spent with social media on adolescents’ self-esteem:

(H1) Overall, adolescents’ self-esteem will increase as a result of their time spent with social media in the past hour.

Heterogeneity in the Effects of Social Media Use on Self-esteem

Most media effects theories that have been developed during and after the 1970s agree that media effects are conditional, meaning that they do not equally hold for all media users (for a review see Valkenburg et al., 2016 ). These theories have sparked numerous media effects studies trying to uncover how certain dispositional, environmental, and contextual variables may enhance or reduce the cognitive, affective, and behavioral effects of media. In the past decade, this media effects research has resulted in an upsurge in meta-analyses of media effects, which not only helped integrating the findings in this vastly growing literature, but also pointed at the moderators that may explain differential susceptibility to media effects.

Despite their undeniable value, the effect sizes for both the main and moderating effects of media use that these meta-analyses have yielded typically range between r = .10 and r = .20 ( Valkenburg et al., 2016 ). Although small to medium effect sizes are common in many neighboring disciplines, some media scholars have argued that such small media effects defy common sense because everyday experience offers anecdotal evidence of strong media effects for some individuals ( Valkenburg et al., 2016 ). Moreover, qualitative studies have repeatedly confirmed that media users differ greatly in their responses to (social) media (e.g., Rideout & Fox, 2018 ). And studies on the emotional reactions to scary media content have reported extreme responses for particular individuals ( Cantor, 2009 ).

There is an apparent discrepancy between the magnitude of conditional media effects sizes reported in quantitative studies and meta-analyses on the one hand and the results of qualitative studies and anecdotal examples on the other. By focusing on group-level moderator effects, meta-analyses (and the studies on which they are based) invariably gloss over more subtle individual differences between people ( Pearce & Field, 2016 ). Diving deeper into these subtle individual differences, however, is only possible with research designs that are able to detect differences in person-specific effects. Such designs require a large number of assessments per person to derive conclusions about processes within single persons, as well as a sufficient number of participants for bottom-up generalization to sub-populations ( Voelkle et al., 2012 ).

An important aim of this study is to capture such person-specific susceptibilities to the effects of SMU by employing a novel method of analysis: Dynamic Structural Equation Modeling (DSEM). DSEM is an advanced modeling technique that is suitable for analyzing intensive longitudinal data, that is, data with 20 to more than 100 repeated measurements that are typically closely spaced in time ( McNeish & Hamaker, 2020 ). DSEM combines the strengths of multilevel analysis and Structural Equation Modeling (SEM) with N  =   1 time-series analysis. N  =   1 time-series analysis enables researchers to establish the longitudinal (lagged) associations between SMU and self-esteem within single persons. The multilevel part of DSEM provides the opportunity to test whether the person-specific effect sizes of SMU on self-esteem differ between persons. Combining the power of a large number of assessments of single persons with a large sample, DSEM may help us answer the question: For how many adolescents does SMU support their self-esteem, for how many does it hinder their self-esteem, and for how many does it not affect their self-esteem?

Not only media effects theories, but also self-esteem theories give reason to assume person-specific effects of environmental influences on self-esteem. These theories agree that some individuals experience significant boosts (or drops) in self-esteem when they experience minor disapproval (or approval) from their peers, whereas the self-esteem of others may fluctuate only in case of serious self-relevant experiences ( Crocker & Brummelman, 2018 ). For example, a study by Harter and Whitesell (2003) showed that 59% of adolescents were prone to self-esteem fluctuations, whereas 41% were not or less prone to such fluctuations. Based on these insights of self-esteem theories, it is likely that the effects of SMU will also differ from adolescent to adolescent. Due to the positivity bias of social media interactions, we expect that most adolescents will experience increases in self-esteem as a result of their SMU in the past hour, whereas a smaller group will experience decreases in self-esteem, and for another smaller group of adolescents their SMU will be unrelated to their self-esteem. Therefore, we hypothesize:

(H2) The effect of time spent with social media on self-esteem will vary from adolescent to adolescent.

Participants

This preregistered study is part of a larger project on the psychosocial consequences of SMU. The present study uses data from the first three-week experience sampling method (ESM) wave of this project that took place in December 2019. The sample consisted of 387 early and middle adolescents (13- to 15-year-olds; 54% girls; M age = 14.11, SD = .69) from a large secondary school in the southern area of The Netherlands. Participants were enrolled in three different levels of education: 44% were in lower prevocational secondary education (VMBO), 31% in intermediate general secondary education (HAVO), and 26% in academic preparatory education (VWO). Of all participants, 96% was born in The Netherlands and self-identified as Dutch, 2% was born in another European country, and 2% in a country outside Europe. The sample was representative of this area in The Netherlands in terms of educational level and ethnic background ( Statistics Netherlands, 2020 ).

The study was approved by the Ethics Review Board of the University of Amsterdam. Before the start of the study parents gave written consent for their child’s participation in the study, after they had been extensively informed about the goals of the study. At the end of November 2019, participants took part in a baseline session during school hours. Researchers informed participants of the aims and procedure of the study and assured them that their responses would be treated confidentially. Participants were provided with detailed instructions about the ESM study that started in the week following upon the baseline survey. They were instructed on how to install the ESM software application (Ethica Data) on their phones, and how to answer the different types of ESM questions. At the end of the baseline session, participants completed an initial ESM survey on their use of different social media platforms, which we used to personalize subsequent ESM surveys. In case of questions or problems with the installment of the software, three researchers were present to help out.

ESM study . In the three-week ESM study, participants completed six 2-minute surveys per day in response to notifications from their mobile phones. The first and last ESM surveys contained 24 questions, whereas each of the other four ESM surveys consisted of 23 questions. Each ESM survey assessed, among other variables not reported in this study, participants’ self-esteem and their SMU. Participants received questions about their time spent with Instagram, WhatsApp, and Snapchat if they had indicated in the baseline session that they used these platforms more than once per week. In case participants did not use any of these platforms more than once a week, they were surveyed about other platforms that they did use (e.g., YouTube or gaming). If they did not use any other platforms either, they received other questions to ensure that each participant received the same number of questions. In total, 375 (97%) participants received questions about WhatsApp, 345 participants (89%) about Instagram, and 285 (73%) about Snapchat.

Sampling scheme . In total, participants received 126 ESM surveys (i.e., 21 days * 6 assessments a day) at random time points within fixed intervals. The sampling scheme was tailored to the school’s schedule and participants’ weekday and weekend routines to avoid that participants received notifications during class hours and while sleeping in on the weekends. Five to ten minutes after each ESM notification, participants received an automatic reminder. We have uploaded our entire notification scheme with the response windows on OSF .

Monitoring plan/incentives. We regularly messaged adolescents to check whether we could help with any technical issues and to motivate them to fill out as many ESM surveys as possible. Adolescents received a small gadget for participating in the baseline session, and a compensation of €0.30 for each completed ESM survey. In addition, each day we held a lottery, in which four participants who had completed all six ESM surveys the day before could win €25.

Compliance. We sent out 48,762 surveys (i.e., 387 × 126) to participants. Due to unforeseen technical problems with the Ethica software, 862 ESM surveys did not reach participants. As a result, 47,900 ESM surveys were received, and 34,930 surveys were completed. This led to a compliance rate of 73%, which is good in comparison with previous ESM studies among adolescents ( van Roekel et al., 2019 ). On average, participants completed 90.26 ESM surveys ( SD = 23.84).

A priori power-analyses. The number of assessments was determined based on the fact that a minimum of 50–100 assessments per participant is recommended to conduct N  =   1 time-series analyses ( Voelkle et al., 2012 ). In order to obtain at least 50 assessments per participant, we took a conservative approach and scheduled for a total of 126 assessments. A priori power analyses indicated that a number of 300 participants would suffice to reliably detect small effect sizes with a minimum power of .80 and significance levels of p = .05.

Time spent with social media . To obtain an ecologically valid ESM assessment of time spent with social media, we asked participants at each assessment how much time in the past hour they had spent with the three most popular platforms: WhatsApp, Instagram, and Snapchat. For each platform, we selected the most popular activities ( van Driel et al., 2019 ). For Instagram, we asked: How much time in the past hour have you spent… (1) sending direct messages on Instagram? (2) reading direct messages on Instagram? (3) viewing posts/stories of others on Instagram? For WhatsApp, we asked: How much time in the past hour have you spent… (4) sending messages on WhatsApp? (5) reading messages on WhatsApp? For Snapchat we asked: How much time in the past hour have you spent… (6) viewing snaps of others on Snapchat? (7) viewing stories of others on Snapchat? (8) sending snaps on Snapchat? Response options for each of these activities were measured with a Visual Analog Scale (VAS) that ranged from 0 to 60 minutes with one-minute intervals.

Participants’ scores on these activities were summed for each of the three platforms. For some assessments this summation led to time estimations exceeding 60 min. For WhatsApp this pertained to 0.85% of all 34,127 assessments, for Instagram to 2.40% of all 31,718 assessments, and for Snapchat to 3.87% of all 26,533 assessments. As indicated in our preregistration , these scores were recoded to 60 min. In a next step, the indicated times spent with WhatsApp, Instagram, and Snapchat were summed to create a variable “time spent with social media.” The summation of the three platforms again led to some estimations exceeding 60 min (i.e., 10.64% of all 34,686 estimations). In accordance with our preregistration, these scores were recoded to 60 min.

Self-esteem. Based on Rosenberg’s (1965) self-esteem scale, and studies establishing the validity of single-item measures of self-esteem (e.g., Robins et al., 2001 ), we presented participants with the question: “How satisfied do you feel about yourself right now?” We used a 7-point response scale ranging from 0 (not at all) to 6 (completely), with 3 (a little) as the midpoint.

Method of Analysis

As preregistered , we employed Dynamic Structural Equation Modeling (DSEM) for intensive longitudinal data in Mplus Version 8.4. Following the recommendations of McNeish and Hamaker (2020) , we estimated a two-level autoregressive lag-1 model (AR[1] model) with self-esteem as the outcome. At the within-person level (level 1), we specified SMU in the past hour as the time-varying covariate of self-esteem (to investigate H1), while controlling for the autoregressive effect of self-esteem (i.e., self-esteem predicted by lag-1 self-esteem). At the between-person level (level 2), we included the latent mean level of self-esteem and the latent mean of SMU in the past hour, and the correlation between these mean levels (to investigate RQ1). Finally, we included the between-person variances around the within-person effects of SMU on self-esteem (i.e., random effects to investigate H2).

Before estimating the model, we checked the required assumption of stationarity, that is, whether the mean of the outcome did not systematically change during the study ( McNeish & Hamaker, 2020 ). To do so we compared a two-level fixed effect model with day of study predicting self-esteem with an intercept-only model (i.e., a model without predictors). The assumption of stationarity was confirmed: Day of the study explained only 0.82% of the within-person variance in self-esteem.

Model specifications . By default, DSEM uses Bayesian Markov Chain Monte Carlo (MCMC) for model estimation. We followed our preregistered plan of analyses and ran the DSEM model with a minimum of 5,000 iterations. Before interpreting the estimates, we checked whether the model converged following the procedure of Hamaker et al. (2018) . Model convergence is considered successful when the Potential Scale Reduction (PSR) values are very close to 1 ( Gelman & Rubin, 1992 ), and the trace plots for each parameter look like fat caterpillars. We interpreted the parameters with the Bayesian credible intervals (CIs), as well as the Bayesian p- values. The hypotheses are confirmed if the 95% CIs for the effect of SMU on self-esteem (within-level; H1) and for the variance around this effect (between-level; H2) do not contain 0. Further details of the analytical strategy can be found in the preregistration of the study.

Correlations and Descriptives

Table 1 presents the means, standard deviations (SDs), ranges, and the within-person, between-person, and intra-class correlations (ICCs) of time spent with social media (SMU) and self-esteem. As the table shows, the average level of self-esteem was high ( M  =   4.09, SD = 1.12, range = 0–6). Participants spent on average almost 17 minutes (range 0–60 min.) with social media in the hour before each measurement occasion. The between-person association of the mean level of SMU with the mean level of self-esteem was significantly negative ( r = −.14, p = .005). The within-person correlation was close to zero ( r = −.01, p = .028), but significant (due to the high power of the study).

Descriptive Statistics and Within-Person, Between-Person, and Intra-Class Correlations of Time Spent with Social Media (SMU) and Self-Esteem

Mean scores reflect average number of minutes spent with social media in the past hour.

Within-person association ( p = .028) between SMU and self-esteem.

between-person association ( p = .005) between SMU and self-esteem.

The Intra-Class Correlations (ICCs) were .45 for self-esteem and .48 for SMU, which means that 45% of the variance in self-esteem and 48% of the variance in SMU was explained by differences between participants (i.e., between-person variance), whereas the larger part of these variances (55% and 52%) was explained by fluctuations within participants (i.e., within-person variance). These ICCs confirm that our sampling scheme of six assessments a day was appropriate for assessing within-person fluctuations in self-esteem and SMU and led to data with sufficient within-person variance for DSEM analyses.

DSEM Results

In all the steps of the analysis strategy, we followed our preregistered plan . We first ran a DSEM model with a minimum of 5,000 iterations (and a default maximum of 50,000 iterations) and one-hour time intervals (TINTERVAL = 1). This model did not converge: The Potential Scale Reduction (PSR) convergence criterion reached 1.354, which is not close enough to 1. As recommended by McNeish and Hamaker (2020) , in a next step, we improved the model setup by increasing the time interval from 1 to 2 hours (TINTERVAL = 2). This model converged well and before the 5,000 iterations. The PSR for this model was 1.006. Visual inspection of the trace plots confirmed that convergence was successful. Finally, we also ran a model with 10,000 iterations to exclude the possibility that the PSR value of 5,000 iterations was close to 1 by chance ( Schultzberg & Muthén, 2018 ). This model reached a PSR of 1.002, and its results did not deviate from the model with 5,000 iterations.

Investigating Research Question and Hypotheses

To answer our research question (RQ1), we investigated the between-person association between SMU and self-esteem. The DSEM analyses revealed a significantly negative association of −.147 between SMU and participants’ level of self-esteem, meaning that participants who spent more time with social media across the three weeks had a lower average level of self-esteem compared to participants who spent less time with social media across this period ( Table 2 ).

DSEM Results of the Between-Person Associations and Within-Person Effects of Time Spent with Social Media (SMU) and Self-Esteem (S-E)

The relationship between SMU and β rβ reflects the extent to which the within-person effect of momentary SMU on momentary S-E depends on the average level of adolescents’ SMU;

The relationship between S-E and β β reflects the extent to which the within-person effect of momentary SMU on momentary S-E depends on adolescents’ average level of S-E;

The 95% Credible Interval of the variance around the effect of SMU on S-E indicates that the within-person effect of SMU on S-E differed among participants. b ’s are unstandardized; β β’s are standardized using the STDYX Standardization in Mplus; p -values are one-tailed Bayesian p -values ( McNeish & Hamaker, 2020 ).

Our first hypothesis (H1) predicted an overall positive within-person effect of SMU on self-esteem. This within-person effect represents the average changes in self-esteem (i.e., self-esteem controlled for self-esteem at t −1) as a result of SMU in the previous hour. This hypothesis did not receive support. Despite the high power of the study, the within-person effect was nonsignificant (β = −.009), meaning that, on average, participants’ self-esteem did not increase nor decrease as a result of their SMU in the previous hour ( Table 2 ).

Our second hypothesis (H2), which predicted that the within-person effect of SMU on changes in self-esteem would differ from participant to participant, did receive support ( Table 2 : random effect = 0.006, p = .000). This random effect means that there was significant variance between participants in the extent to which their SMU in the previous hour predicted changes in their self-esteem.

Figure 1 shows the distribution of the person-specific standardized effect sizes for the effect of SMU on changes in self-esteem. These effect sizes ranged from β = −.21 to β = +.17 across participants. As the bar graph shows, the majority of participants (88%) experienced no or very small positive or negative effects of their SMU (i.e., −.10 < β < .10) on changes in self-esteem, whereas a small group of participants (4%) experienced positive (.10 ≤ β ≤ .17), and another small group (8%) experienced negative effects (−.21 ≤ β ≤ -.10) of SMU on changes in self-esteem. Figure 2 presents the N  =   1 time-series plots of three participants, one who experienced a positive, one who experienced a negative, and one who experienced a null-effect of SMU on self-esteem.

Range of the Standardized Person-Specific Effects of SMU on in Self-Esteem.

Note. The vertical black line represents the mean of the person-specific effects ( β = −.009).

Three N = 1 time-series plots picturing the effects of SMU on self-esteem (S-E).

Note . The x -axes represent the measurement moments (range 1–126). The y -axes represent the co-fluctuations in SMU (blue lines, range 0–60 minutes/10) and S-E (yellow lines, range 0–6). The top plot belongs to a participant who experienced a positive effect of SMU on S-E ( β = .174). The SMU and S-E of this participant regularly co-fluctuated (e.g., around moment 40 and around moment 41). The middle plot is from a participant who experienced a negative effect ( β β = −.196): When the SMU of this participant increased, his/her S-E dropped (e.g., around moment 56), and vice versa (e.g., around moment 21). The bottom plot is from a participant who experienced no effects ( β = .013): At some moments, the S-E of this participant increased after his/her SMU increased (e.g., around moment 45), at othermoments her/his S-E dropped after his/her SMU went up (e.g., moment 72), resulting in a net effect close to zero.

Exploratory Analyses

In addition to our preregistered hypotheses, we ran four exploratory analyses. In a first step, we investigated potential platform differences. Because earlier studies into the relationship between SMU and self-esteem did not investigate differential effects of different platforms, we summed adolescents’ use of Instagram, Snapchat, and WhatsApp to create our SMU measure. To explore potential platforms differences, we reran our analyses separately for each of the three platforms. Our results did not show significant differences in the between-person relationships and within-person effects of the use of these platforms on self-esteem (see Supplement 1).

In a second step, we ran a multilevel model without controlling for self-esteem at the previous assessment. Given that DSEM models are rather stringent and that sizeable differences in effect sizes between lagged and non-lagged media effects have been reported ( Adachi & Willoughby, 2015 ), we wanted to get insight into these differences. All other model specifications of the multilevel model were identical to the initial DSEM model. The associations between SMU and self-esteem in the multilevel model ranged from β = −.34 to β = +.33. Consistent with the DSEM model, the average within-person association of SMU and self-esteem was close to zero (β = −.007, p = .162, CI = [−0.022, 0.007] compared to β = −.009 in the DSEM model).

In a third step, we explored whether the person-specific within-person effects of SMU on self-esteem (i.e., the βs) differed for adolescents with different mean levels of SMU or different mean levels of self-esteem. As Table 2 shows, the cross-level interaction of participants’ mean levels of SMU with the β’s was non-significant, indicating that adolescents with higher mean levels of SMU did not experience a more negative (or positive) within-person effect of SMU on their self-esteem than their peers with lower SMU. The cross-level interaction of self-esteem and the βs did reveal that the within-person effect of SMU on self-esteem depended on adolescents’ mean level of self-esteem: Adolescents with lower average levels of self-esteem had a more positive within-person effect of SMU on self-esteem than adolescents with higher average levels of self-esteem, and vice versa.

In a final step, we investigated a between-person hypothesis of one of the anonymous reviewers, who suggested to check whether adolescents with moderate SMU would experience higher trait levels of self-esteem than those with low and high SMU. We investigated this potential inverted U-shaped relationship between SMU and self-esteem by following the two-step hierarchical regression analysis used by Cingel and Olsen (2018) . At step 1 of this regression analysis, we found a negative linear relationship between SMU and self-esteem (β = − .145, p = .005; R 2 = .021, see also Table 1 ). At step 2, we found no significant curvilinear relationship between SMU and self-esteem, because the added squared SMU term did not result in a significant change in the explained variance (Δ R 2 = .001, Δ F (1, 380) = .516, p = .473).

Sensitivity Analysis

As preregistered , we conducted a validation check to examine whether participants’ answers were trustworthy according to the following criteria: (1) inconsistency of participants’ within-person response patterns, (2) outliers, (3) unserious responses (e.g., gross comments) to the open question in the ESM study. Based on these criteria, we considered the responses of eight participants as potentially untrustworthy, because they violated criterion 1 and 2 ( n  =   4) or criterion 1 and 3 ( n  =   4). As a sensitivity analysis, we reran the DSEM analysis without these eight participants. The results of both the between-person and within-person associations did not deviate from those of the full sample.

The two existing meta-analyses on the relationship of SMU and self-esteem assessed the effects of their included empirical studies as weak and their results as mixed ( Huang, 2017 ; Liu & Baumeister, 2016 ). The between-person associations reported in empirical studies on SMU and self-esteem ranged from +.22 ( Apaolaza et al., 2013 ) to − .28 ( Rodgers et al., 2020 ). In the current study, the between-person association between SMU and self-esteem fits within this range: We found a negative relationship of r = − .15 between SMU and self-esteem (RQ1), meaning that adolescents who spent more time on social media across a period of three weeks reported a lower level of self-esteem than adolescents who spent less time on social media. This negative relationship pertained to the summed usage of Instagram, Snapchat, and WhatsApp, but did not differ for the usage of each of the separate platforms.

In addition, although we hypothesized a positive overall within -person effect of SMU on self-esteem (H1), we found a null effect. However, this overall null effect must be interpreted in light of the supportive results for our second hypothesis (H2), which predicted that the effect of SMU on self-esteem would differ from adolescent to adolescent. We found that the majority of participants (88%) experienced no or very small positive or negative effects of SMU on changes in self-esteem ( − .10 < β < .10), whereas one small group (4%) experienced positive effects (.10 ≤ β ≤ .17), and another small group (8%) negative effects of SMU ( − .21 ≤ β ≤ − .10) on self-esteem.

The person-specific effect sizes reported in the current study pertain to SMU effects on changes in self-esteem (i.e., self-esteem controlled for previous levels of self-esteem). As Adachi and Willoughby (2015 , p. 117) argue, such effect sizes are often “dramatically” smaller than those for outcomes that are not controlled for their previous levels. Indeed, when we checked this assumption of Adachi & Willoughby, the associations between SMU and self-esteem not controlled for its previous levels resulted in a considerably wider range of effect sizes (β = − .34 to β = +.33) than those that did control for previous levels (β = − . 21 to β = +.17). To account for a potential undervaluation of effect sizes in autoregressive models, Adachi and Willoughby (2015 , p. 127) proposed “a more liberal cut-off for small effects in autoregressive models (e.g., small = .05).” In this study, we followed our preregistration and interpreted effect sizes ranging from − .10 < β < +.10 as non-existent to very small. However, if we would apply the guideline proposed by Adachi and Willoughby (2015) to our results, the distribution of effect sizes would lead to 21% negative susceptibles, 16% positive susceptibles, and 63% non-susceptibles.

Our results showed that the effects of SMU on self-esteem are unique for each individual adolescent, which may, in turn, explain why the two meta-analyses evaluated the effects of their included studies as weak and their results as inconsistent. First, our results suggest that these effects were weak because they were diluted across a heterogeneous sample of adolescents with different susceptibilities to the effects of SMU. This suggestion is supported by comparing our overall within-person effect (β = − .01, ns) with the full range of person-specific effects, which ranged from moderately negative to moderately positive. Second, the effects reported in earlier studies may have been inconsistent because these studies may, by chance, have slightly oversampled either “positive susceptibles” or “negative susceptibles.” After all, if a sample is somewhat biased towards positive susceptibles, the results would yield a moderately positive overall effect. Conversely, if a sample is somewhat biased towards negative susceptibles the results would report a moderately negative overall effect.

It may seem reassuring at first sight that the far majority of participants in our study did not experience sizeable negative effects of SMU on their self-esteem. However, as illustrated in the bottom N  =   1 time-series plot in Figure 2 , for some participants, their non-significant within-person effect may result from strong social media-induced ups and downs in self-esteem, which cancelled each other out across time, resulting in a net null effect. However, as the two upper time-series plots in Figure 2 show, not only the non-susceptibles, but also the positive and negative susceptibles sometimes experienced effects in the opposite direction: The positive susceptibles occasionally experienced negative effects, while the negative susceptibles occasionally experienced positive effects.

Although DSEM models enable researchers to demonstrate how within-person effects of SMU differ across persons, they do not (yet) allow us to statistically evaluate the presence of both positive and negative effects within one and the same person (Hamaker, 2020, personal communication). A possibility to analyze the combination of positive and negative effects within persons may soon be offered by even more advanced modeling strategies than DSEM, which are currently undergoing a rapid development. Among those promising developments are regime switching models ( Lu et al., 2019 ), which provide the opportunity to establish the co-occurrence of both positive and negative effects of SMU within single persons.

Explanatory Hypotheses and Avenues for Future Research

Although our study allowed us to reveal the prevalence of positive susceptibles, negative susceptibles, and non-susceptibles among participants, it did not investigate why and when some adolescents are more susceptible to SMU than others. Our exploratory results did show that adolescents with a lower mean level of self-esteem, experienced a more positive within-person effect of SMU on self-esteem than adolescents with a higher mean level of self-esteem. This latter result may point to a social compensation effect ( Kraut et al., 1998 ), indicating that adolescents who are low in self-esteem may successfully seek out social media to enhance their self-esteem. Our DSEM analysis did not reveal differences in the within-person effects of SMU on self-esteem among adolescents with high and low SMU, suggesting that the positive effects among some adolescents cannot be attributed to modest SMU, whereas the negative effects among other adolescents cannot be attributed to excessive SMU.

An important next step is to further explain why adolescents differ in their susceptibility to SMU. A first explanation may be that adolescents differ in the valence (the positivity or negativity) of their experiences while spending time on social media. It is, for example, possible that the positive susceptibles experience mainly positive content on social media, whereas the negative susceptibles experience mainly negative content. In this study, we focused on time as a predictor of momentary ups and downs in self-esteem. However, most self-esteem theories emphasize that it is the valence rather than the duration of social experiences that results in self-esteem fluctuations. It is assumed that self-esteem goes up when we succeed or when others accept us, and drops when we fail or when others reject us ( Leary & Baumeister, 2000 ). Future research should, therefore, extend our study by investigating to what extent the valence of experiences on social media accounts for differences in susceptibility to the effects of SMU above and beyond adolescents’ time spent on social media.

A second explanation as to why adolescents differ in their susceptibility to the effects of SMU may lie in person-specific susceptibilities to the positivity bias in SM. Our first hypothesis was based on the idea that the sharing of positively biased information would elicit reciprocal positive feedback from fellow users, which, in turn, would lead to overall improvements in self-esteem. However, our results suggest that, for some adolescents, this positivity bias may lead to decreases in self-esteem, for example, because of their tendency to compare themselves to other social media users who they perceive as more beautiful or successful. This tendency towards social comparison may lead to envy (e.g., Appel et al., 2016 ) and decreases in self-esteem ( Vogel et al., 2014 ).

Until now, studies investigating the positive feedback hypothesis have mostly focused on the positive effects of feedback on self-esteem (e.g., Valkenburg et al., 2017 ), whereas studies examining the social comparison hypothesis have mainly focused on the negative effects of social comparison on self-esteem (e.g., Vogel et al., 2014 ). However, both the positive feedback hypothesis and the social comparison hypothesis are more complex than they may seem at first sight. First, although most adolescents receive positive feedback while using social media, a minority frequently receives negative feedback ( Koutamanis et al., 2015 ), and may experience resulting decreases in self-esteem. Likewise, although social comparison may lead to envy, it may also lead to inspiration (e.g., Meier & Schäfer, 2018 ), and resulting increases in self-esteem. Future research should attempt to reconcile these explanatory hypotheses by investigating who is particularly susceptible to positive and/or negative feedback, and who is particularly susceptible to the positive (e.g., inspiration) and/or negative (e.g., envy) effects of social comparison on social media.

Another possible explanation for differences in person-specific effects of SMU on self-esteem may lie in differences in the specific contingencies on which adolescents’ self-esteem is based. Self-esteem contingency theory ( Crocker & Brummelman, 2018 ) recognizes that people differ in the areas of life that serve as the basis of their self-esteem ( Jordan & Zeigler-Hill, 2013 ). For example, for some adolescents their physical appearance may serve as the basis of their self-esteem, whereas others may base their self-esteem on peer approval. Different contexts may also activate different self-esteem contingencies ( Crocker & Brummelman, 2018 ). On the soccer field, athletic ability is valued, which may activate the athletic ability contingency in this context. On social media, physical appearance and peer approval may be relevant, so that these contingencies may particularly be triggered in the social media context. It is conceivable that adolescents who base their self-esteem on appearance or peer approval may be more susceptible to the effects of SMU than adolescents who base their self-esteem less on these contingencies, and this is, therefore, another important avenue for future research.

Stimulating Positive and Mitigating Negative Effects

Our results suggest that for the majority of adolescents the momentary effects of SMU are small or negligible. As discussed though, all adolescents—whether they are positive susceptibles, negative susceptibles, or non-susceptibles—may occasionally experience social media-induced drops in self-esteem. Social media have become a fixture in adolescents’ social life, and the use of these media may thus result in negative experiences among all adolescents. Therefore, not only the negative susceptibles, but all adolescents need their parents or educators to help them prevent, or cope with, these potentially negative experiences. Parents and educators can play a vital role in enhancing the positive effects of SMU and combatting the negative ones. Helping adolescents prevent or process negative feedback and explaining that the social media world may not be as beautiful as it often appears, are important ingredients of media-specific parenting as well as school-based media literacy programs.

Although this study was designed to contribute to (social) media effects theories and research, our analytical approach may also have social benefits. After all, N  =   1 time-series plots could not only be helpful for theory building, but also for person-specific advice to adolescents. These plots give a comprehensive snapshot of each adolescent’s experiences and responses across more or less prolonged time periods. Such information could greatly help tailoring prevention and intervention strategies to different adolescents. After all, only if we know which adolescents are more or less susceptible to the negative and positive effects of social media, are we able to adequately target prevention and intervention strategies at these adolescents.

Towards a Personalized Media Effects Paradigm

Insights into person-specific susceptibilities to certain environmental influences is burgeoning in several disciplines. For example, in medicine, personalized medicine is on the rise. In education, personalized learning is booming. And in developmental psychology, differential susceptibility theories are among the most prominent theories to explain heterogeneity in child development. Although N  =   1 or idiographic research is now progressively embraced in multiple disciplines, spurred by recent methodological developments, it has a long history behind it. In fact, in the first two decades of the 20th century, scholars such as Piaget, Pavlov, and Thorndike often conducted case-by-case research to develop and test their theories bottom up (i.e., from the individual to the population; Robinson, 2011 ). However, in the 1930s, idiographic research soon lost ground to nomothetic approaches, certainly after Francis Galton attached the term nomothetic to the aggregated group-based methodology that is still common in quantitative research ( Robinson, 2011 ). However, due to technological advancements, it has become feasible to collect masses of intensive longitudinal data from masses of individuals on the uses and effects of social media (e.g., through ESM, tracking). Moreover, rapid developments in data mining and statistical methods now also enable researchers to analyze highly complex N  =   1 data, and by doing so, to develop and investigate media effects and other communication theories bottom-up rather than top-down (i.e., from the population to the individual). We hope that this study may be a very first step to a personalized media effects paradigm.

Additional Supporting Information may be found in the online version of this article.

This study was funded by an NWO Spinoza Prize and a Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to Patti Valkenburg by the Dutch Research Council (NWO). Additional funding was received from a VIDI grant (NWO VIDI Grant 452.17.011) awarded to Loes Keijsers.

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• Published: 06 July 2023

Pros & cons: impacts of social media on mental health

• Ágnes Zsila 1 , 2 &
• Marc Eric S. Reyes   ORCID: orcid.org/0000-0002-5280-1315 3  

BMC Psychology volume  11 , Article number:  201 ( 2023 ) Cite this article

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The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.

Social media has become integral to our daily routines: we interact with family members and friends, accept invitations to public events, and join online communities to meet people who share similar preferences using these platforms. Social media has opened a new avenue for social experiences since the early 2000s, extending the possibilities for communication. According to recent research [ 1 ], people spend 2.3 h daily on social media. YouTube, TikTok, Instagram, and Snapchat have become increasingly popular among youth in 2022, and one-third think they spend too much time on these platforms [ 2 ]. The considerable time people spend on social media worldwide has directed researchers’ attention toward the potential benefits and risks. Research shows excessive use is mainly associated with lower psychological well-being [ 3 ]. However, findings also suggest that the quality rather than the quantity of social media use can determine whether the experience will enhance or deteriorate the user’s mental health [ 4 ]. In this collection, we will explore the impact of social media use on mental health by providing comprehensive research perspectives on positive and negative effects.

Social media can provide opportunities to enhance the mental health of users by facilitating social connections and peer support [ 5 ]. Indeed, online communities can provide a space for discussions regarding health conditions, adverse life events, or everyday challenges, which may decrease the sense of stigmatization and increase belongingness and perceived emotional support. Mutual friendships, rewarding social interactions, and humor on social media also reduced stress during the COVID-19 pandemic [ 4 ].

On the other hand, several studies have pointed out the potentially detrimental effects of social media use on mental health. Concerns have been raised that social media may lead to body image dissatisfaction [ 6 ], increase the risk of addiction and cyberbullying involvement [ 5 ], contribute to phubbing behaviors [ 7 ], and negatively affects mood [ 8 ]. Excessive use has increased loneliness, fear of missing out, and decreased subjective well-being and life satisfaction [ 8 ]. Users at risk of social media addiction often report depressive symptoms and lower self-esteem [ 9 ].

Overall, findings regarding the impact of social media on mental health pointed out some essential resources for psychological well-being through rewarding online social interactions. However, there is a need to raise awareness about the possible risks associated with excessive use, which can negatively affect mental health and everyday functioning [ 9 ]. There is neither a negative nor positive consensus regarding the effects of social media on people. However, by teaching people social media literacy, we can maximize their chances of having balanced, safe, and meaningful experiences on these platforms [ 10 ].

We encourage researchers to submit their research articles and contribute to a more differentiated overview of the impact of social media on mental health. BMC Psychology welcomes submissions to its new collection, which promises to present the latest findings in the emerging field of social media research. We seek research papers using qualitative and quantitative methods, focusing on social media users’ positive and negative aspects. We believe this collection will provide a more comprehensive picture of social media’s positive and negative effects on users’ mental health.

Data Availability

Not applicable.

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Acknowledgements

Ágnes Zsila was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

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The impact of social media on self-esteem.

Published online by Cambridge University Press:  01 September 2022

The Social media have gained tremendous popularity over the past decade, these sites have occupied a major part of people’s lives, especially young people. Many teenagers use tik tok, instagram, snapchat and facebook, to build relationships, connect with the world, share and acquire knowledge and information, and build their personalities, their effects are not limited. to that, comparisons made using social networking sites have led people to have a drop in self-esteem, with all the complications that can cause (anxiety disorders, depression and the anxiety disorder , etc.)

Assessment of the impact of social media on the self-image, of young subjects in the Moroccan context

Cross-sectional study with a descriptive and analytical aim, using a questionnaire and a satisfaction scale to assess the impact of social media on the self-image of young subjects in the Moroccan context. bibliographic research to objectify several studies on this subject

our results are close to the results of the literature. Sample of 200 young peoples was selected based on the confidence level of 80%. In order to test the hypothesis each respondent was given a questionnaire which tested their selfesteem and enquired the amount of time they spent on Facebook, instagram, tik tok, snapchat.

social networks are a way to communicate information, ideas of ways of life. this communication includes harmful effects on the social behavior of young people

No significant relationships.

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• Volume 65, Special Issue S1
• F.Z. Chamsi (a1) , I. Katir (a1) , A. Korchi (a1) , S. Belbachir (a1) and A. Ouanass (a1)
• DOI: https://doi.org/10.1192/j.eurpsy.2022.1410

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How social media can crush your  self-esteem

Candidate au doctorat en psychologie, Université du Québec à Montréal (UQAM)

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Sabrina Laplante does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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We all have a natural tendency to compare ourselves to others, whether intentionally or not, online or offline. Such comparisons help us evaluate our own achievements , skills, personality and our emotions. This, in turn, influences how we see ourselves.

But what impact do these comparisons have on our well-being? It depends on how much comparing we do.

Comparing ourselves on social media to people who are worse off than we are makes us feel better . Comparing ourselves to people who are doing better than us, however, makes us feel inferior or inadequate instead . The social media platform we choose also affects our morale, as do crisis situations like the COVID-19 pandemic.

As a PhD student in psychology, I am studying incels — men who perceive the rejection of women as the cause of their involuntary celibacy. I believe that social comparison, which plays as much a role in these marginal groups as it does in the general population, affects our general well-being in the age of social media.

An optimal level of comparison

The degree of social comparison that individuals carry out is thought to affect the degree of motivation they have. According to a study by researchers at Ruhr University in Bochum, Germany, there is an optimal level of perceived difference between the self and others that maximizes the effects of social comparison.

Specifically, if we see ourselves as vastly superior to others, we will not be motivated to improve because we already feel that we are in a good position. Yet, if we perceive ourselves as very inferior, we will not be motivated to improve since the goal seems too difficult to achieve.

In other words, the researchers note, beyond or below the optimal level of perceived difference between oneself and another, a person no longer makes any effort. By perceiving oneself as inferior, the individual will experience negative emotions, guilt and lowered pride and self-esteem.

Unrealistic comparisons on social media

Social comparisons therefore have consequences both for our behaviour and for our psychological well-being. However, comparing yourself to others at a restaurant dinner does not necessarily have the same effect as comparing yourself to others on Facebook. It is easier to invent an exciting existence or embellish certain aspects of things on a social media platform than it is in real life .

The advent of social media, which allows us to share content where we always appear in our best light, has led many researchers to consider the possibility that this amplifies unrealistic comparisons.

Research shows that the more time people spend on Facebook and Instagram, the more they compare themselves socially. This social comparison is linked, among other things, to lower self-esteem and higher social anxiety.

A study conducted by researchers at the National University of Singapore explains these results by the fact that people generally present positive information about themselves on social media. They can also enhance their appearance by using filters, which create the impression that there is a big difference between themselves and others.

In turn, researchers working at Facebook observed that the more people looked at content where people were sharing positive aspects of their lives on the platform, the more likely they were to compare themselves to others .

COVID-19: Less negative social comparison

However, could the effect of this comparison in a particularly stressful context like the COVID-19 pandemic be different?

A study from researchers at Kore University in Enna, Italy, showed that before lockdowns, high levels of online social comparison were associated with greater distress, loneliness and a less satisfying life. But this was no longer the case during lockdowns .

One reason for this would be that by comparing themselves to others during the lockdown, people felt they were sharing the same difficult experience. That reduced the negative impact of social comparisons. So, comparing oneself to others online during difficult times can be a positive force for improving relationships and sharing feelings of fear and uncertainty.

A different effect depending on the social media

There are distinctions to be made depending on which social media platform a person is using. Researchers at the University of Lorraine, France, consider that social media platforms should not be all lumped together .

For example, the use of Facebook and Instagram is associated with lower well-being, while Twitter is associated with more positive emotions and higher life satisfaction. One possible explanation: Facebook and Instagram are known to be places for positive self-presentation, unlike Twitter, where it is more appropriate to share one’s real opinions and emotions.

Trying to get social support on social media during the COVID-19 pandemic may reactivate negative emotions instead of releasing them, depending on which social media platform a person is using.

Many things motivate us to compare ourselves socially. Whether we like it or not, social media exposes us to more of those motivations. Depending on the type of content that is being shared, whether it is positive or negative, we tend to refer to it when we are self-evaluating. Sharing content that makes us feel good about ourselves and garners praise from others is nice, but you have to consider the effect of these posts on others.

Yet overall, I believe that sharing your difficulties in words, pictures or videos can still have positive effects and bring psychological benefits.

This article was originally published in French

• Social media
• Relationships
• Life satisfaction
• Social anxiety disorder
• Coronavirus
• Self-esteem
• Uncertainty
• psychological well-being

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The Impact of Social Media on Self-Esteem

Social media is a platform which helps to connects the world. It is a computer tool which allows people to create, share or exchange ideas, information, images, videos and other forms of expression via virtual communities and networks. Social media has immense popularity and its power has long lasting effects on people and leads to change in behavior. This research paper examines the relationship between the social media and its impact on behavioral change of the youth. Adolescence and young adulthood are crucial stage in development where youth begin to form their own identity and create meaningful relationships but the usage of social media can impact on area of their development hence this paper evaluates the impact of social media on self-esteem of youth. Thispresent study is based on both primary and secondary data. Simple random sampling is used to select the samples of the study. The analysis of data is been done with simple statistics using the MS-Excel.

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This paper focuses on how the social media effects on user's self esteem. The paper's main goal is to investigate the relationship between social media and user's self esteem factor. The paper collected data from a number of the most active social media users to participate with probably random sampling system. Data was collected with the use of a questionnaire that contained closed-ended questions. This paper has been effectuated by the examination of the events such as the the effect of social network sites on adolescents' social and academic development: current theories and controversies. The paper examines the relationship between social network and social capital, privacy, youth safety, psychological well-being, and educational achievement. Last paper explored the social network sites effects on active social media user's social and academic development, this research highlights the importance relations of the user's self esteem and the affected physiologycal, safety, love, and self actualization need factors.

Dr Nawaz Ahmad , Muqaddas Jan , Sanobia Anwar Soomro

Social media has gained immense popularity in the last decade and its power has left certain long-lasting effects on people. The upward comparisons made using social networking sites have caused people to have lower self-esteems. In order to test the hypothesis 150 students from institute of business management were surveyed through questionnaires and interviews. This research was limited to the students of IoBM and Facebook, being the most popular social networking site was used as the representative of social media. Correlation and regression model was applied to the data with the help of SPSS statistics to test the relationship between social media and self-esteem. The major findings suggest that approximately 88% people engage in making social comparisons on Facebook and out of the 88%, 98% of the comparisons are upward social comparisons. Further this research proves there that there is a strong relationship between social media and self-esteem. Increase in social media usage causes the self-esteem of individuals to decrease. One hour spent on Facebook daily results in a 5.574 decrease in the self-esteem score of an individual.

Chew Yee Herng, Carlson

Influence of Social media on self-perception amongst the youth.

Joshua Horlortu Makafui

A cross sectional survey was used to study the relationship between social media and self-perception among the youth on the University of Ghana. One hundred and twenty (120) participants were conveniently sampled from University of Ghana, Legon – Accra. E-questionnaires measuring social media influence and self-perception were digitally filled by participants to collect data. The results clearly showed that there was a positive association between social media and self-perception. The results however were discussed with limitations, implications and recommendations for future research.

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The use of wide variety of social media is in demand by adolescent in both urban and rural today. Social media makes adolescents easy to express feeling and post various activities. The uses of social media by adolescents in the rural have a difference with adolescents in urban areas. The use of social media can influence self-concept and social adjustment of adolescent in their environment. This research aims to know relationship between social media user and self-concept at SMPN 2 Singingi Hilir Riau. This research is a descriptive correlation research with cross sectional design. There are 252 respondents as samples. Collecting data of this research are using questioners from Social Networking Time Use Scale (SONTUS), Sriati Academic Self-concept (SASC), and Social Adjustment. In analyzing data the research uses Rank Spearman correlation test to observe relationship between two variables and canonical trials in order to determine the most dominant factor related to social media usage. The result of this research shows that majority of adolescent who use social media at SMPN 2 is low; most of them have positive self-concept and a quite good in social adjustment. The result of the correlation analysis shows that variables of social media usage have weak relationship between self-concept variable (rs = 0,224) and social adjustment variable (rs = 0,254). Based on result of canonic analysis, self-concept is a dominant factor in case in social media relation.

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• DOI: 10.1192/j.eurpsy.2022.1410
• Corpus ID: 251955957

The impact of social media on self-esteem

• F. Chamsi , I. Katir , +2 authors A. Ouanass
• Published in European psychiatry 1 June 2022

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Impact of social media on self-esteem and body image among young adults

R. molina ruiz.

1 Hospital clínico san carlos, Psychiatry, Madrid, Spain

I. Alfonso-Fuertes

2 Comillas University, Psychology, Madrid, Spain

S. González Vives

Introduction.

The extent to which social media contributes to body image dissatisfaction and lower self-esteem is currently under debate

This research seeks to study the relationship between the use of Instagram (one of the main platforms used by young people nowadays) and the degree of dissatisfaction with body image and the level of self-esteem among their younger users

A sample of 585 Spanish adults between 18 and 40 years old was used, to whom the Body Shape Questionnaire (BSQ), the Rosenberg Self-esteem Scale and the Social Comparison of Appearance Scale (PACS) were applied.

A positive correlation was observed between the frequency of use of the social network and dissatisfaction with body image and low self-esteem. In addition, it was found that content observation time significantly predicts body dissatisfaction and low self-esteem. On the other hand, the type of content both published and observed, showed no effect on any of these variables, although it has been found that the relationship between the use of the platform and the study variables seems to be mediated by the tendency of their users to compare their appearance with others.

Conclusions

These results invite us to reflect on the use of social networks and their impact on constructs as relevant to the person as self-esteem and body image and on how different policies should be taken into account to prevent a negative impact on the mental health of their users

No significant relationships.

Impact of Social Media on Self Esteem

This essay about the impact of social media on self-esteem examines the complex effects of online platforms. It discusses how social media serves as a tool for connection and self-expression, providing users with a sense of community and support. However, it also highlights the darker aspects, such as the pressures of unrealistic comparisons and the quantification of social worth through likes and followers. These elements can lead to feelings of inadequacy and anxiety, particularly among younger users. The essay also addresses the issue of cyberbullying and its detrimental effects on self-esteem. It concludes with suggestions for navigating social media healthily, including developing a critical perspective on online content, valuing offline interactions, and fostering a supportive digital environment through education and policy.

How it works

In the vast and varied world of social media, where a single post can reach the eyes of thousands in an instant, we find ourselves more connected than ever before. Yet, as we scroll through feeds filled with snapshots of seemingly perfect lives, it’s easy to question where we fit into this digital tapestry. The influence of social media on self-esteem is profound and multi-dimensional, affecting everything from how we see ourselves to how we interact with the world around us.

Initially, social media platforms like Facebook, Instagram, and Twitter were heralded as revolutionary tools for broadening social circles and expressing oneself. For many, they have delivered on this promise. These platforms allow us to keep in touch with distant friends and family, share significant life events, and express our thoughts and creativity. For those who might feel isolated in their immediate physical surroundings, social media can offer a vital connection to like-minded communities, whether they’re gaming enthusiasts, beauty gurus, or fitness buffs. This aspect of social media can be incredibly uplifting, providing affirmation and support that might not be available offline.

Yet, this digital social sphere is also a double-edged sword. As we navigate through streams of content from others, each post can act as a mirror reflecting back not just who we are, but who we ought to be. The ‘compare and despair’ phenomenon kicks in as users subconsciously measure their own lives against the idealized images others post. This comparison is seldom fair or realistic, as social media often showcases a curated version of life—highlight reels meticulously edited and presented for public consumption. The resulting feeling? A nagging sense of inadequacy that can eat away at one’s self-esteem.

These feelings of inadequacy are often exacerbated by the quantifiable nature of validation in these virtual spaces. Success on social media is frequently measured by likes, comments, and follower counts. It’s easy to fall into the trap of equating these numbers with personal worth, turning what could be a fun, social activity into a relentless pursuit of approval. For adolescents and young adults, whose self-identity is still in flux, this can lead to a significant impact on self-esteem. They might chase an elusive image of perfection, only to find themselves more anxious and less happy.

Moreover, social media is not just a stage for passive observation but also an arena of active interaction. While many interactions are positive, these platforms are equally ripe for negative exchanges such as cyberbullying and trolling. Unlike traditional bullying, cyberbullying offers no physical escape as the digital domain is omnipresent. Harsh comments, often hidden behind the veil of anonymity, can be particularly vicious and damaging to self-esteem. What’s worse, the viral nature of social media can magnify a single negative comment or post, making it feel like the whole world is watching and judging.

So, how do we navigate this complex landscape? First, it’s crucial for users to develop a critical eye towards the content they consume on social media. Understanding that what appears online is often a polished version of reality can help mitigate feelings of inadequacy. It’s also important for us to learn to value ourselves beyond the metrics of social media. By investing in offline relationships and pursuits, we can build a sense of self-worth that is not dependent on online validation.

Moreover, parents, educators, and policymakers also play a crucial role in shaping a healthier social media environment. Educating young people about the impacts of social comparison, encouraging respectful online behavior, and providing tools to manage online interactions responsibly can help foster a more positive social media experience.

In conclusion, while social media can certainly enhance our sense of connection and community, it can also challenge our self-esteem in fundamental ways. The key lies in using these platforms mindfully and maintaining a balanced perspective. By doing so, we can enjoy the benefits of social connectivity without falling prey to its pitfalls. In a world increasingly driven by digital interactions, cultivating a strong and positive sense of self both online and offline is more important than ever.

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ORIGINAL RESEARCH article

Too much social media unveiling the effects of determinants in social media fatigue.

• 1 School of Music, Jiangxi Normal University, Nanchang, China
• 2 Department of Arts Management, Xinghai Conservatory of Music, Guangzhou, China
• 3 Faculty of Humanities and Social Sciences, City University of Macau, Macau, Macao SAR, China
• 4 College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou, Henan, China
• 5 School of Music and Dance, Jiangxi University of Technology, Nanchang, China
• 6 Design College, Zhoukou Normal University, Zhoukou, Henan, China

Introduction: With the boom in social media, many people spend a lot of time on these platforms. Among them, some developed negative emotions, such as fatigue, depression, or disinterest in communicating, and used social media temporarily or permanently. Therefore, this study aims to explore the antecedents of social media fatigue, including social media helpfulness, social media self-efficacy, online subjective well-being, social comparison, compulsive social media use, privacy concerns, fear of missing out, and information overload, and to further discuss the determinants of social media fatigue on social anxiety and lurking.

Methods: An online questionnaire was distributed to social media users, and 659 valid samples were obtained with the help of a purposive sampling strategy. The data was analyzed by the partial least square (PLS) method.

Results: The study found that social media self-efficacy had a significant negative effect on social media fatigue; compulsive social media use, fear of missing out, and information overload had a significant positive effect on social media fatigue; and social media fatigue had a significant positive effect on social anxiety and lurking.

Discussion: The research results can be used as a reference for social media marketers and internet service providers in developing business strategies.

1 Introduction

Today, users are becoming accustomed to using social media to send and deliver messages and video calls ( Nesi et al., 2018 ). With the popularity of social media and the increase in user dependence, it has become a part of people’s lives ( Xie et al., 2021 ). On the other hand, the outbreak of COVID-19 has had a huge impact on people’s lives. In response to the crisis brought about by the epidemic, many countries have adopted a series of preventive measures to avoid the spread of the virus. These measures include social distancing, remote working and learning, and postponement or cancelation of events or meetings ( Ares et al., 2021 ). With more and more activities taking place online, social media is now an effective and important way for users to get reliable information about global pandemics and health advice ( Pang, 2021 ).

Nowadays, tech-savvy young people make up the majority of social media users, but they often experience greater information overload in digital media environments ( Pang, 2020 , 2021 ; Liu H. et al., 2021 ; Xu et al., 2021 ). As time spent on social media increases, excessive use of social media may have physical health effects such as mental fatigue, stress, and anxiety. Research has indicated that individuals are avoiding participation in these communication services due to social media fatigue ( Whelan et al., 2020 ; Pang, 2021 ). Users do not necessarily have a strong psychological quality to resist information overload, leading to subjective fatigue and withdrawal from social media use ( Lee et al., 2016 ; Pang, 2019 ).

Clement (2020) pointed out that 93% of organizations have adopted social media as a tool in their marketing strategies and have generated huge advertising revenue, which is expected to grow by 28.4% by 2022. Research has further found that the use of social media in sales is positively correlated with salespeople’s customer knowledge, sales behavior, and performance ( Rodriguez et al., 2016 ; Itani et al., 2017 ). Social media can help organizations collect and process various customer information, thereby enabling companies to adjust their products to suit different preferences of customers ( Woodcock et al., 2011 ).

As social media usage continues to rise, consumers are beginning to experience social media fatigue. Since social media does not create content, social media marketing is entirely dependent on user-generated content to survive and thrive ( Liu et al., 2020 ). Therefore, when social media fatigue leads to reduced, suspended, or discontinued usage, or lurking to use social media without delivering content, it can lead marketers to worry that brand advertising on social media is less effective. For social network services (SNS) providers, as users reduce or withdraw from social media use, they will expect lower long-term profits ( Dhir et al., 2018 ). Scholars believe that social media fatigue has a significant negative impact on users, businesses, and service providers ( Oghuma et al., 2016 ; Shin and Shin, 2016 ). Fatigue can cause users to drop out of services, resulting in lower profits for companies and service operators.

Finally, a growing body of research has highlighted the potential relationship between increased social media use and various forms of negative mental health ( Luqman et al., 2017 ; Dhir et al., 2018 ; Logan et al., 2018 ; Abi-Jaoude et al., 2020 ; Pang, 2021 ). Previous studies have pointed out that users’ strategies in the face of social media fatigue include intentions to transfer, pause, exit, and interrupt the platform ( Ravindran et al., 2014 ; Shin and Shin, 2016 ; Luqman et al., 2017 ). However, few researches have examined lurking as a result of social media fatigue. Therefore, this study regards lurking as a consequence of social media fatigue and explores the factors that lead to social media fatigue and the psychological and behavioral effects on users. The research purpose is to explore the determinants and consequences of social media fatigue. Thus, several research questions are proposed, including (1) the factors that cause users’ social media fatigue, (2) the impact of social media fatigue on users’ psychology, and (3) the impact of social media fatigue on social anxiety and lurking.

2 Literature review and hypothesis development

The primary theoretical framework for this study on social media fatigue encompasses cognitive load theory (CLT), social cognitive theory, and social comparison theory. These theories offer an in-depth understanding of the psychological and emotional factors that lead to social media fatigue. Cognitive load theory, suggests that individuals have a finite capacity for processing information ( Sweller and Chandler, 1991 ; Sweller, 2023 ). In the realm of social media, users often encounter an overwhelming amount of information, resulting in cognitive overload and subsequent fatigue ( Kirschner et al., 2018 ). This theory clarifies why information overload and compulsive use of social media are pivotal antecedents of social media fatigue ( Chen et al., 2023 ). Additionally, social comparison theory, asserts that people assess their social and personal worth by comparing themselves to others ( Festinger, 1954 ; Powdthavee, 2024 ). On social media, this frequent comparison can lead to negative self-assessments and fatigue ( De Vries et al., 2023 ). This theory supports the inclusion of social comparison and FOMO as key antecedents in this research ( Gupta et al., 2021 ). Lastly, this research also employed social cognitive theory which indicates an individual’s confidence in coping with life stress and achieving performance ( Chou et al., 2024 ). This theory supports the social media self-efficacy antecedent of this research ( Almulla and Al-Rahmi, 2023 ). The selected antecedents—social media helpfulness, social media self-efficacy, online subjective well-being, social comparison, compulsive social media use, privacy concerns, FOMO, and information overload—are grounded in these theories ( Rezabeigi Davarani et al., 2023 ; Sweller, 2023 ). Collectively, they provide a solid theoretical foundation for exploring the determinants and consequences of social media fatigue ( Jabeen et al., 2023 ). The research aims to understand how these factors contribute to fatigue and its effects on social anxiety and lurking behaviors, offering valuable insights for social media marketers and internet service providers.

2.1 Social media fatigue

Social media fatigue comes from the word “fatigue.” Several medical studies have suggested that fatigue is a psychosomatic response and a series of phenomena of self-evaluation and stress perception ( Wijesuriya et al., 2007 ; Pang, 2021 ). Other researches define social media fatigue as a subjective and multidimensional user experience, including tiredness, annoyance, anger, disappointment, caution, loss of interest, or low need/motivation to interact with others on Social media ( Ravindran et al., 2014 ; Zhang et al., 2016 ; Teng et al., 2022 ).

In other words, excessive and compulsive use of social media, or perceived information overload on social media, may lead to users becoming tired of social media activity, a phenomenon known as social media fatigue ( Ravindran et al., 2014 ; Bright and Logan, 2018 ). Because people rely heavily on Social media to connect with others and search heavily for information about the outbreak. Users are exposed to excessive and ambiguous information on social media, resulting in fatigue ( Islam et al., 2021 ). Additionally, scholars have argued that social media fatigue is harmful to both users and service providers ( Shin and Shin, 2016 ). For example, if users continue to use social media, their boredom and lack of enthusiasm may lead to lower engagement ( Pang et al., 2024 ). Furthermore, users with social media fatigue may experience discontinuous or interrupted use behavior ( Luqman et al., 2017 ; Fu et al., 2020 ; Liu Z. et al., 2021 ). In addition, social media fatigue is closely related to the health of the mind and body. Dhir et al. (2018) and Pang (2021) indicated that it causes negative psychological effects on users, such as depression, anxiety, emotional stress, and social anxiety. In conclusion, this study proposes that determinants of social media fatigue include social media helpfulness, social media self-efficacy, online subjective well-being, social comparison, compulsive social media use, privacy concerns, fear of missing out, and information overload; the consequences are social anxiety and lurking. Next, each of these determinants and consequences is described and the research hypotheses are developed.

2.2 Determinants of social media fatigue

2.2.1 social media helpfulness.

Today, social media (e.g., Facebook, Twitter, and Instagram) have become one of the ways users communicate with each other. They provide users with the functionality and helpfulness to engage in conversations, share ideas, form relationships, and interest groups, and develop their presence, reputation, and identity ( Kietzmann et al., 2011 ). Social media helpfulness refers to the extent to which users receive resources and useful information from exploring social media ( Bright and Logan, 2018 ). Users perceive social media to be useful because they satisfy needs, such as communicating with others, finding friends, keeping up-to-date, and being entertained on social media ( Naranjo-Zolotov et al., 2019 ; Taylor et al., 2022 ).

Foster et al. (2010) mentioned that people use social media because of their informative value. In other words, users feel that using social media is helpful to them. In addition, Logan et al. (2018) believe that users can obtain resources and useful information from social media, and then perceive the social media’s helpfulness. Therefore, this study proposes a hypothesis.

H1 : Social media helpfulness is negatively related to social media fatigue.

2.2.2 Social media self-efficacy

Bandura (1977) defines self-efficacy as the belief that an individual can organize and perform a specific action. Also, self-efficacy is a component of social cognitive theory and can be thought of as an individual’s confidence in coping with life stress and achieving performance ( Schwarzer et al., 1997 ; Alshahrani and Rasmussen Pennington, 2018 ). In addition, scholars have pointed out that people with high confidence are more likely to take action and stick with it, and they are also willing to adopt new technologies or search for useful information ( Stajkovic, 2006 ; Hocevar et al., 2014 ). In short, self-efficacy affects behavior ( Bandura, 1986 ).

Research has found that media use experience has a positive effect on self-efficacy ( Eastin and LaRose, 2000 ). As a result, users’ perceived ability to use social media increases, and their willingness to share information increases, resulting in happier feelings ( Lenhart et al., 2010 ). In addition, Bearden and Netemeyer (1999) proposed social media confidence as the ability of users to perceive their ability to process content on social media. Hocevar et al. (2014) argued that social media self-efficacy is the degree to which users perceive expected results to be achieved in social media. Logan et al. (2018) believe that users perceive social media self-efficacy, and their confidence will increase their willingness to use social media. In conclusion, this study suggests that social media users are less likely to experience social media fatigue when they perceive social media self-efficacy. Therefore, a hypothesis is proposed.

H2 : Social media self-efficacy is negatively related to social media fatigue.

2.2.3 Online subjective well-being

Subjective well-being is defined as a broad phenomenon that includes people’s emotional responses, domain satisfaction, and overall judgments of life satisfaction ( Diener et al., 1999 ). It has two important components, including emotional well-being, which assesses an individual’s mood, and cognitive well-being ( Russell and Daniels, 2018 ), which measures an individual’s life satisfaction ( Verduyn et al., 2017 ). Emotional well-being is measured by pleasant emotions (e.g., joy, happiness, ecstasy) or unpleasant emotions (e.g., guilt, sadness, stress); cognitive well-being is measured based on one’s satisfaction with life ( Di Martino et al., 2018 ). Changes in information technology can also affect subjective well-being. The popularity of information and communication technology in the media has improved people’s well-being ( Graham and Nikolova, 2013 ), but it also occupies the time when people maintain relationships with friends, which indirectly has a negative impact on subjective well-being ( Bruni and Stanca, 2008 ).

Online subjective well-being is defined as the broad range of feelings and emotions experienced by individuals using the internet and social media, such as satisfaction, well-being, and negative and positive affect ( Verduyn et al., 2017 ; Fan et al., 2019 ; Kaur et al., 2021 ; Pang and Zhang, 2024 ). Huang (2016) mentioned that online subjective well-being refers to personal well-being, perceived social support, and satisfaction with online or social media life, and online social well-being has a strong impact on the continued use intention of personal social media. Previous studies have suggested that subjective well-being can be negatively affected by social media use ( Gerson et al., 2016 ; Yao and Cao, 2017 ). Kaur et al. (2021) developed a research framework to examine the relationship between online subjective well-being and social media fatigue. They found that individuals who perceived higher online subjective well-being may experience lower fatigue due to their ability to properly balance and process social media communications.

Previous research examined subjective well-being as a consequence of social media use ( Gerson et al., 2016 ). Satici and Uysal (2015) pointed out that life satisfaction and subjective well-being are negatively correlated with adverse social media use symptoms. Kaur et al. (2021) believe that users’ satisfaction and high perceived benefits from social media enable them to have higher cognitive processing ability to deal with information and content on social media, thereby experiencing low social media fatigue. In other words, social media users with high online subjective well-being experienced fewer negative phenomena, such as fatigue. In addition, previous studies have shown that social media use and personal subjective well-being are negatively correlated with negative emotions (e.g., jealousy, depression, psychological burden) ( Tandoc et al., 2015 ; Verduyn et al., 2015 ), which in turn reduce life satisfaction ( Frison and Eggermont, 2016 ) and make social media less attractive to users ( De Vries and Kühne, 2015 ). In summary, this study proposes a hypothesis.

H3 : Online subjective well-being is negatively related to social media fatigue.

2.2.4 Social comparison

Social comparison theory (SCT) assumes that individuals may engage in two forms of social comparison, upward and downward. People assess their current abilities and ideas by comparing themselves to those who are better off (upward) or worse off (downward) ( Festinger, 1954 ; Kim and Chock, 2015 ). In the absence of objective information, people have an intrinsic drive to compare themselves with others, often to gain an accurate self-evaluation. Social media provides a wealth of easily accessible information and thus can serve as a new way for people to engage in social comparisons ( Burnell et al., 2019 ). On the other hand, if users of social media cannot have a perception of their abilities, they will compare themselves with others ( Festinger, 1954 ; Talwar et al., 2019 ). Individuals compare themselves to others when confronted with information about others, such as their occupations, abilities, and achievements ( Mussweiler et al., 2006 ). Social comparison in social media refers to the process in which individuals compare their abilities and opinions with others by browsing various information disclosed by others in the process of using social media ( Yang et al., 2018 ). They may perceive others to be relatively better placed in the community than they are and make upward social comparisons ( Latif et al., 2021 ).

Cramer et al. (2016) believe that comparing with others is a human tendency. Although SCT assumes that individuals can make upward and downward comparisons. However, studies exploring social media have shown that individuals tend to make more negative social comparisons, which can lead to decreased well-being, such as depressive symptoms ( Faranda and Roberts, 2019 ). Song et al. (2019) explained that sharing content such as videos and photos on social media to positively present themselves favorably can lead others to see their positive but distorted lives. Lim and Choi (2017) found that when social comparison becomes a stressor for using social media, it may lead to emotional exhaustion in the user experience. Based on previous research findings, this study proposes a hypothesis.

H4 : Social comparison is positively related to social media fatigue.

2.2.5 Compulsive social media use

Compulsive behavior, or compulsive use, is a repetitive addiction, such as compulsive buying, overeating, or excessive use of online social media, that can have negative personal and social consequences. Compulsive use emphasizes the abnormal behavior of individuals who are unable to rationally control or regulate their daily performance ( Gámez-Guadix et al., 2012 ; Venkatesh et al., 2019 ; Zhang et al., 2020 ). In social media research, compulsive use is often associated with internet addiction disorder (IAD) ( Venkatesh et al., 2019 ). Unger et al. (2018) demonstrated that compulsive behavior is an addictive process in which vulnerable individuals seek escape from stress and anxiety and engage in frequent recreational and leisure activities. Despite intentional efforts to discourage or reduce compulsive behavior, it tends to persist ( Gong et al., 2019 ).

Masur et al. (2014) found that social media addiction often leads to wasted time, reduced social connections, lower work and school performance, loss of control, and withdrawal syndrome. Compulsive use is primarily explored within a range of unhealthy physiological behaviors, including smoking or alcohol abuse, gaming addiction, and specific social media overuse ( Soroya et al., 2021 ). Samaha and Hawi (2016) believe that smartphone addiction has a negative impact on mental health and well-being, and users with higher addiction risks experience higher perceived stress, which in turn reduces life satisfaction and academic performance. Dhir et al. (2018) used a stressor-stress-outcome (SSO) framework to explore the relationship between mental health and compulsive social media use on social media fatigue during the COVID-19 pandemic. They found that compulsive social media use significantly induced social media fatigue, which in turn led to anxiety and depression. Pang’s (2021) research also obtained similar results. Compulsive social media use is one of the major contributors to social media fatigue.

Ho et al. (2014) found that excessive internet use can lead to anxiety and depression. SNS exhaustion is a psychological consequence of excessive use of social media, resulting in low satisfaction. This phenomenon reflects individuals’ psychological responses (e.g., stress) to social media use ( Maier et al., 2015 ). Elhai et al. (2016) found that compulsive mobile phone use affects people’s behavior and social interactions. Additionally, Dhir et al. (2018) found that compulsive social media use negatively affects cognition and performance and contributes to social media fatigue. According to previous studies, compulsive media use is positively correlated with social media fatigue ( Islam et al., 2021 ; Mamun et al., 2021 ). Based on the above, this study proposes a hypothesis.

H5 : Compulsive social media use is positively related to social media fatigue.

2.2.6 Privacy concerns

With the growth of social media, online privacy is a major concern for many users. The popularity of social media and the internet has also raised concerns about privacy and security, so privacy issues are becoming more and more important. Personal privacy concerns refer to the fear that one’s personal information will be collected and misused by others, and cannot be fully protected ( Stewart and Segars, 2002 ). Stutzman et al. (2011) believe that people who are more concerned about the improper use of personal information will engage in privacy protection behaviors. Bright and Logan (2018) mentioned that as users continue to share more personal information, privacy concerns will become their primary consideration when using social media and applications. Lee and Hsieh (2013) observed that privacy concerns are one of the components of fatigue.

Logan et al. (2018) pointed out that people with high social media self-efficacy tend to perceive the helpfulness of social media, and at the same time they will become more and more aware of privacy concerns, leading to social media fatigue. Users of social media may worry about the impact of their disclosure on their reputation in social media, leading to fatigue ( Lee et al., 2019 ). Bright and Logan (2018) found that people who are highly concerned about privacy are prone to social media fatigue. According to past studies, high levels of privacy concerns consume social media users’ cognition and may translate into fatigue ( Talwar et al., 2019 ; Malik et al., 2020 ; Kaur et al., 2021 ). Therefore, the hypothesis is proposed.

H6 : Privacy concerns are positively related to social media fatigue.

2.2.7 Fear of missing out

Fear is an unpleasant emotion that can damage people’s mental health. When fear is excessive, it can lead to phobias and social anxiety ( Mertens et al., 2020 ). Fear of missing out (FoMO) is defined as worry or fear of being disconnected, absent, or missing out on experiences that others (e.g., peers, friends, family) might have or enjoy. When experiencing FoMO, people may be persistently and eagerly seeking and acknowledging the activities of others, for example, constantly checking social media content, and checking whether friends are attending parties they were not invited ( Przybylski et al., 2013 ). The concept of FoMO applies offline, in real life, and online social media. FoMO is a constant state of mental flow. Users’ FoMO drives social media use, yet creates a sense of missing out ( Przybylski et al., 2013 ; Tandon et al., 2021 ). Based on the SSO framework, FoMO is one of the important stressors that put social media users under mental and emotional stress, which in turn triggers undesirable behaviors (e.g., avoidance) ( Zhang et al., 2020 ).

FoMO has been explored in past studies discussing social media ( Whelan et al., 2020 ; Tandon et al., 2021 ). Bright and Logan (2018) found that FoMO can lead to fatigue in individuals. In addition, Tandon et al. (2021) believe that if users continue to use social media due to FoMO, they will be overloaded with information and cause fatigue. Based on the above, this study proposes a hypothesis.

H7 : Fear of missing out is positively related to social media fatigue.

2.2.8 Information overload

With the development of information technology, there are more channels for individuals to obtain a large amount of information than before. The negative results brought about by too much information have also attracted increasing attention from researchers ( Luqman et al., 2017 ). Humans have a limited ability to process information, and information that exceeds this ability will lead to performance degradation ( Hunter, 2004 ). Information overload is defined as a situation in which a large amount of input information exceeds the information processing capacity of an individual ( Jones et al., 2004 ; Soto-Acosta et al., 2014 ; Guo et al., 2020 ; Islam et al., 2021 ). Various social media have been used as sources of crisis events and related information during COVID-19 ( Islam et al., 2021 ). At the same time, young people frequently and excessively participate in social media activities and continuously obtain various COVID-19 information from there, which may lead to an overload of relevant information and lead to adverse psychological consequences ( Liu Z. et al., 2021 ; Soroya et al., 2021 ). In addition, large amounts of information can be generated and disseminated rapidly on social media. Information overload occurs when people are exposed to more information than they can process efficiently ( Maier et al., 2015 ; Zhang et al., 2016 ).

The limited capacity mode shows that individuals have limited resources to process information. Lang (2000) believed that information overload has an impact on social media fatigue. In a social media environment, users acquire vast amounts of information ( Bright and Logan, 2018 ). However, the stress of social media-induced information overload can lead to emotional fatigue in users. When users cannot effectively integrate, absorb, and utilize too much information, it will have an impact on work, life, and interpersonal relationships ( Zhang et al., 2021 ). In conclusion, information overload on social media may trigger user fatigue ( Ravindran et al., 2014 ; Lee et al., 2016 ). Thus, the hypothesis is proposed.

H8 : Information overload is positively related to social media fatigue.

2.3 Consequences of social media fatigue

2.3.1 social anxiety.

Schlenker and Leary (1982) defined social anxiety as the anxiety that individuals feel when they are concerned about interpersonal evaluation when they make a specific impression on those they talk to in real or virtual social situations. Social anxiety refers to the pervasive and debilitating experience of discomfort and avoidance of interpersonal interactions due to fear of being negatively judged, rejected, or embarrassed ( Panayiotou et al., 2020 ; Islam et al., 2021 ; Ran et al., 2022 ). Previous studies have pointed out that social anxiety is an important emotional factor, which is closely related to mobile phone addiction ( Annoni et al., 2021 ). In addition, some studies related to the Internet have explored social anxiety ( Hwang et al., 2020 ; Pitcho-Prelorentzos et al., 2020 ; Cao et al., 2022 ), arguing that information overload can affect emotional stress through social media fatigue and social anxiety ( Pang, 2021 ).

In recent years, researchers have begun to explore the social anxiety of social media users. Scholars believe that when experiencing fatigue, users’ cognitive abilities decline, thereby predisposing them to inadequate regulation and control of emotions and attention, such as anxiety ( Grieve et al., 2013 ; Fox and Moreland, 2015 ; Zhang et al., 2020 ). Social anxiety, considered a negatively reactive emotion, is a cognitive, psychological, and behavioral anxiety disorder associated with cognitive dysfunction and fatigue ( Keles et al., 2020 ). When users experience social media fatigue, the psychological and physical effects are profound, including emotional anxiety and decreased life satisfaction and productivity ( Dhir et al., 2018 ). Alkis et al. (2017) developed and verified the social anxiety scale of social media users, and found that undergraduate students have social anxiety caused by social media, and have higher social anxiety for SNS. Social media fatigue refers to negative emotional responses to activities on social media such as tiredness, burnout, exhaustion, frustration, and lack of interest in communicating. Based on previous literature, this study proposes the following hypothesis.

H9 : Social media fatigue is positively related to social anxiety.

2.3.2 Lurking

Social media users have shown mental and psychological deterioration due to social media fatigue ( Dhir et al., 2018 ). Thus, users facing social media fatigue are more willing to change their status quo and existing unhealthy status ( Maier et al., 2015 ). Lurking is associated with non-posting behavior and is defined as inactive online user behavior. They rarely post, are silent, do not participate, or have not been involved and contributed to online activities ( Nonnecke and Preece, 2001 ). These users become social media lurkers ( Sun et al., 2014 ). And lurking behavior can influence others to become lurkers ( Zhang et al., 2021 ). Moreover, Rui and Stefanone (2013) believe that some users who find it difficult to adapt to the diversity of social media will overload their information, making users unable to cope effectively and choose to be lurkers. Lurking was perceived by users as a safer and easier social strategy for coping with such distress. Wisniewski et al. (2014) argue that, for social media users, lurking acts as a maladaptive countermeasure to reduce their short-term stress at the expense of increasing long-term stress.

Researchers have found that social media fatigue may be an important driver of discontinuous use intentions ( Ravindran et al., 2014 ; Zhang et al., 2016 ). The variety of information and social demands on social media can overwhelm users’ processing capabilities. Users can experience fatigue after expending too much energy dealing with these demands. Lurking behaviors induced by social media fatigue include ignorance, avoidance, and withdrawal ( Zhang et al., 2020 ). Users may use the above behaviors to escape negative emotions and fatigue ( Khan, 2017 ). Based on the above findings, this study puts forward the following hypothesis.

H10 : Social media fatigue is positively related to lurking.

2.4 Social media fatigue as a mediator

The rationale for selecting social media fatigue as a mediator in this research lies in its links to both the antecedents and outcomes of this study. Empirical studies have shown that variables such as information overload, compulsive use of social media, social comparison, and FOMO are direct contributors to social media fatigue ( Przybylski et al., 2013 ; Bright et al., 2015 ; Dhir et al., 2018 ). Cognitive load theory posits that the cognitive burden from excessive information and compulsive behaviors leads to fatigue ( Sweller, 2023 ), while social comparison theory suggests that social comparisons and FOMO result in emotional depletion ( Powdthavee, 2024 ). These antecedents are specifically tied to social media fatigue, making it a more appropriate construct for the unique context of social media use. Additionally, existing research indicates that social media fatigue is a predictor of behaviors such as lurking and psychological states like social anxiety ( Świątek et al., 2021 ; Hong et al., 2023 ). Consequently, social media fatigue is used as the mediator because it effectively represents the mental and emotional stress associated with social media, providing a solid theoretical and empirical foundation for examining how these antecedents lead to the identified outcomes. Hence, this research aims to examine the several indirect relationships generated from the theoretical framework with social media fatigue being a mediating variable.

Through a literature review, this study attempts to identify the determinants that influence social media fatigue, and its possible consequences, then formulate hypotheses and construct a research model (see Figure 1 ).

Figure 1 . Research model.

3 Research method

3.1 research design.

Based on the identified characteristics, the researchers defined the target population consisting of individuals who spent a significant amount of time on social media, engaged in frequent social media interactions, or exhibited behaviors indicative of compulsive social media use. The researchers employed a purposive sampling technique, which is characterized by the deliberate selection of participants possessing certain qualities that are of interest to the researcher. In this study, the researchers purposively selected participants through social media platforms known for high levels of user engagement, such as Facebook, Instagram, or Twitter based on their social media usage patterns, and targeted individuals who exhibited behaviors indicative of potential susceptibility to social media fatigue.

The study used an online questionnaire and posted the URL of the questionnaire on social media. In addition, to improve the recovery of valid questionnaires, this study also commissioned a professional academic questionnaire company to distribute. The questionnaire was distributed from February 8, 2021 to March 9, 2022. Each questionnaire was answered anonymously. Finally, a total of 659 valid questionnaires were collected. The demographics of the respondents are shown in Table 1 .

Table 1 . Demographic statistics ( N  = 659).

The research questionnaire was divided into two parts, containing questions related to social media use and demographics (e.g., gender, age, occupation, education, and most used social media). Questions about social media use are based on previous research. The questions on social media helpfulness and self-efficacy were taken from Bright et al. (2015) ; the questions on online subjective well-being were referenced from Ahn and Shin (2013) , Brunstein (1993) , Chang and Hsu (2016) , and Diener et al. (2015) ; the questions on social comparison were referenced from Gibbons and Buunk (1999) , Latif et al. (2021) , Reer et al. (2019) , and Talwar et al. (2019) ; the questions on compulsive social media use are taken from Panda and Jain (2018) ; the questions on privacy concerns are taken from Dhir et al. (2018) and Malhotra et al. (2004) ; the questions on FoMO were taken from Przybylski et al. (2013) ; the questions on information overload were taken from Zhang et al. (2016) ; the questions on social media fatigue were taken from Dhir et al. (2018) , Islam et al. (2021) , and Whelan et al. (2020) ; the questions on social anxiety were taken from Alkis et al. (2017) ; and the questions on lurking were taken from Osatuyi (2015) . The measurement scale was a seven-point Likert scale, ranging from 1 for “strongly disagree” to 7 for “strongly agree.” Respondents were asked to answer based on their own experience. Also, this study sought advice from experts to improve the quality of the questionnaire. The questionnaire is provided in the Supplementary Material section of the research.

The research analyzed the data in two steps by employing a partial least squares (PLS) methodology. Firstly, the analysis regarding the convergent and discriminant validity of constructs was analyzed ( Anderson and Gerbing, 1988 ; Rahardja et al., 2023 ). In the second step, the analysis regarding path coefficients and hypotheses was conducted. This study selected the PLS methodology because of its capability to analyze relationships ( Petter et al., 2007 ) and complicated frameworks ( Chin and Newsted, 1999 ; Tao et al., 2022 ).

4 Research results

4.1 reliability and validity.

This study applied Partial Least Squares (PLS) to test the measurement model and validate the research model. First, the reliability was tested by Composite Reliability (CR) and Cronbach’s Alpha. Hulland (1999) suggested that CR should be greater than 0.7, indicating that the measured variables are internally consistent. The CR of the latent variable in this study was between 0.780 and 0.950 (see Table 2 ), which was greater than the recommended value (0.7), indicating a good level and internal consistency of the measurement constructs. Hair et al. (2017) suggested that Cronbach’s Alpha is greater than 0.7, indicating that the constructs have good reliability. Table 2 shows that except for Cronbach’s Alpha for the construct “lurking,” which is less than 0.7, the others range from 0.765 to 0.934 (see Table 2 ), which means that the questionnaire has good reliability.

Table 2 . Reliability and validity analysis.

Next, this study examined convergent validity and discriminant validity. Discriminant Validity refers to the degree of correlation between different constructs. When the correlation between the constructs is low, it means that the constructs are different from each other, i.e., they have discriminant validity. The purpose of measuring convergent validity is to ensure that all questions in a construct have a high correlation with that construct. This study used PLS to test Factor Loading and Average Variance Extracted (AVE). The factor loadings ranged from 0.516 to 0.971 (see Table 2 ), which was greater than the recommended value (0.5) by Hair et al. (2017) , indicating that the questions had convergent validity. In addition, this study had questions regarding the eight determinants of social media fatigue. One of the questions on social media helpfulness resulted in an AVE lower than Fornell and Larcker’s (1981) suggested value (0.5) and was removed. The AVE ranged from 0.542 to 0.822, indicating that the constructs had convergent validity. In addition, the correlations between the other constructs were smaller than the square root of the AVE for each construct, indicating discriminant validity (see Table 3 ). In addition to the Fornell-Larker Discriminant Validity, this study further tested the discriminant validity with the Heterotrait-Monotrait Ratio (HTMT). Table 4 shows that the HTMT ranged from 0.050 to 0.881, which is smaller than the value suggested by Henseler et al. (2015) (0.900), indicating that this study had discriminant validity. Table 5 further indicates the cross-loadings of the constructs. The highlighted values indicate that a cross-loading value for a specific construct will be the highest in the latent structure in comparison to other values. Hence, the cross-loadings further reaffirm a satisfactory discriminant validity for the constructs of this study.

Table 3 . Fornell-Larker discriminant validity.

Table 4 . Heterotrait-Monotrait ratio (HTMT).

Table 5 . Cross-loadings.

4.2 Structural equation modeling analysis

After testing the reliability and validity of the measurement model, the hypothesis testing analysis was performed on the structural model. This study used SmartPLS as the analytical tool for hypothesis testing. The main method was the explained variation (R 2 ) to measure the fitness of the research model, and the standardized path coefficient and p -value to determine whether the hypotheses were supported.

Table 5 shows the results of the hypothesis testing. Social media self-efficacy had a negative significant effect on social media fatigue ( β  = −0.115, p  < 0.05); compulsive social media use, FoMO, and information overload had a positive significant effect on social media fatigue ( β  = 0.108, p  < 0.01; β  = 0.121, p  < 0.01; β  = 0.612, p  < 0.001). Therefore, H2, H5, H7, and H8 were supported. However, social media helpfulness, online subjective well-being, social comparison, and privacy concerns had no significant effect on social media fatigue ( β  = 0.090, p  > 0.05; β  = −0.093, p  > 0.05; β  = −0.057, p  > 0.05; β  = 0.050, p  > 0.05). Therefore, H1, H3, H4, and H6 were not supported. Finally, social media fatigue had a positive and significant effect on social anxiety ( β  = 0.367, p  < 0.001) and lurking ( β  = 0.636, p  < 0.001), indicating that H9 and H10 were supported ( Table 6 ).

Table 6 . Direct effect analysis.

R 2 represents the ability of the dependent variable to be explained by the independent variable, or the percentage of the variance that can be explained by the exogenous variables compared to the endogenous variables. R 2 is between 0 and 1. The closer it is to 1, the better the explanatory power. Figure 2 shows that the explanatory power of social media fatigue is 59.0% ( R 2  = 0.590), social anxiety is 40.4% ( R 2  = 0.404), and lurking is 13.4% ( R 2  = 0.134).

Figure 2 . Research results.

In addition, this study also used the results from SMART PLS to indicate indirect relationships. According to the findings indicated in Table 7 . OSWB ( β  = −0.058, T -value = 1.764), PC ( β  = 0.031, T -value = 1.466), SC ( β  = −0.037, T -value = 1.468), and HF ( β  = 0.050, T -value = 1.845) did not have significant indirect relationships with SA, while having SMF as a mediator. Furthermore, SE ( β  = −0.066, T -value = 2.078), CSMU ( β  = 0.067, T -value = 2.843), FOMO ( β  = 0.078, T -value = 2.629), and IO ( β  = 0.390, T -value = 13.253) were found to significantly impact SA indirectly via SMF.

Table 7 . Indirect relationships.

Moreover, OSWB ( β  = −0.034, T -value = 1.721), PC ( β  = 0.018, T -value = 1.449), SC ( β  = −0.022, T -value = 1.418), and HF ( β  = 0.029, T -value = 1.822) did not indirectly impact LU, while having SMF as a mediator. Lastly, SE ( β  = −0.038, T -value = 2.064), CSMU ( β  = 0.039, T -value = 2.669), FOMO ( β  = 0.045, T -value = 2.436), and IO ( β  = 0.226, T -value = 8.155) were found to have significant indirect relationships with LU via SMF.

5 Discussion

5.1 conclusion.

The research purpose is to explore the determinants and consequences of social media fatigue when users use social media. Through data analysis, this research has obtained some conclusions, which are explained as follows.

First, social media self-efficacy was found to have a significant negative impact on social media fatigue. The result can be compared to an earlier study by Liu and He (2021) . According to Liu and He’s (2021) study, social media has become an integral part of people’s lives. While enjoying the benefits of online communication, many young individuals are experiencing various challenges such as negative comparisons, too much information, and difficulties in interacting with others. As a result, social media fatigue (SMF) is emerging among young people. Liu and He’s (2021) study investigated the factors contributing to SMF through a questionnaire survey. Liu and He’s (2021) study identified several factors such as negative comparisons, social media self-efficacy, and information overload that significantly contributed to SMF.

Moreover, the research results showed that compulsive social media use and FoMO had significant positive impacts on social media fatigue. The results of the present study can be compared to an earlier study by Dhir et al. (2018) . According to Dhir et al.’s (2018) study the rise of social media has led to increased users but also fatigue. Dhir et al.’s (2018) study investigated links between well-being and fatigue. It used a framework to examine triggers and outcomes. The data was collected from Indian adolescent users. Dhir et al.’s (2018) findings indicated that compulsive use led to fatigue, then anxiety and depression. Furthermore, the fear of missing out indirectly predicted fatigue.

On the other hand, information overload has a significant positive impact on social media fatigue. This result is similar to a previous study by Pang (2021) . According to Pang’s (2021) study social media supports during pandemics like COVID-19, but its negative impacts are understudied. Pang’s (2021) study explored the effects on well-being, focusing on WeChat and information overload. Pang’s (2021) study collected the data from 566 young individuals. Pang’s (2021) study indicated that overload triggers fatigue, leading to stress and anxiety. Social media fatigue is the feeling of overwhelm, burnout, and fatigue caused by users receiving too much information from social media ( Li et al., 2024 ). However, they also worry about not keeping up with current events and may not be able to communicate with their peers. Their chronic fear and stress of not having the same experience as others can lead to fatigue.

Consequently, the research results also showed that social media helpfulness had no impact on social media fatigue. The research results can be compared to a study conducted by Bright et al. (2015) . According to Bright et al.’s (2015) study social media usage rise can cause social media fatigue. Bright et al.’s (2015) study used Lang’s model to examine information overload’s role. Bright et al.’s (2015) research explored fatigue’s antecedents including efficacy, helpfulness, confidence, and privacy concerns. According to the findings of Bright et al.’s (2015) study social media helpfulness negatively impacted social media fatigue, while privacy concerns and confidence were the top predictors of fatigue.

Furthermore, according to the present study the perceived online subjective well-being of social media users did not impact social media fatigue. This study argues that some social media users may be dissatisfied with the online community and network environment, and thus unable to use them appropriately and reduce fatigue ( Zhao and Khan, 2021 ). The present study’s result can be compared to a study conducted by Kaur et al. (2021) . According to Kaur et al.’s (2021) study scholars focus on social media’s dark impact on well-being. Kaur et al.’s (2021) study employed the limited-capacity model. Kaur et al.’s (2021) study explored the US social media users’ fatigue and collected data from Prolific Academic. Kaur et al.’s (2021) study results showed that online subjective well-being related positively to self-disclosure and social comparison, while negatively correlated with social media fatigue.

Additionally, social comparison has no significant impact on social media fatigue. This study argues that upward social comparison on social media may trigger benign jealousy and thus impact positive behavioral intentions. For example, when a friend has a superior life status on social media, it is positively related to behavioral intentions of self-enhancement and self-improvement through virtuous envy ( Latif et al., 2021 ). Social media fatigue was not significant because comparisons with others did not cause a psychological burden. The result can be compared to a previous study by Jabeen et al. (2023) . According to Jabeen et al.’s (2023) study social media’s prevalence leads to FoMO and fatigue. However, there was a lack of knowledge about their influence on users’ psychology. Jabeen et al.’s (2023) study filled this gap by examining FoMO stimuli. Jabeen et al.’s (2023) study also investigated narcissism’s impact on self-disclosure and social comparison. Jabeen et al.’s (2023) study collected data from social media users in the US. Jabeen et al.’s (2023) study results indicated that FoMO was linked to time cost and anxiety and also influenced narcissistic admiration and rivalry processes. Furthermore, Jabeen et al.’s (2023) study also indicated that social comparison positively affected fatigue.

On the other hand, the impact of privacy concerns on social media fatigue was not significant. Jang and Sung (2021) believe that although privacy concerns are related to the use of online services, highly creative users will still accept and use innovations and actively use online services. This study infers that although the website requires users to provide personal information, users who have the awareness of protecting their basic personal information will not fill in unnecessary information, and thus will not cause fatigue. Another reason is that some social media are only used by users to connect and interact with others ( Malik et al., 2020 ). In other words, users can set their personal social media accounts to private and strictly control followers to prevent private information from being disclosed to unknown users.

Furthermore, the present research results showed that social media fatigue had a positive and significant impact on social anxiety. Social media fatigue can lead to increased social anxiety among social media users, which can be compared to previous research by Świątek et al. (2021) . According to Świątek et al.’s (2021) study several interdisciplinary literatures explored social media fatigue’s correlates, including anxiety and FoMO. Świątek et al.’s (2021) study examined FoMO’s role in the anxiety-social media fatigue link. The data for Świątek et al.’s (2021) study was collected online from 264 participants, mostly women. Świątek et al.’s (2021) results indicated that higher trait anxiety is related to more intense social media fatigue. Furthermore, FoMO mediated the anxiety-social media fatigue association across dimensions.

Lastly, according to the present study, social media fatigue was found to significantly impact lurking. The result can be compared to an earlier research by Hong et al. (2023) . According to Hong et al.’s (2023) study lurking surpasses interaction in social network app usage. Hong et al.’s (2023) study scrutinized lurking behavior and its drivers. Hong et al.’s (2023) study examined information refusal, browsing, and fatigue. Hong et al.’s (2023) research collected insights from 786 questionnaires and highlighted fatigue and refusal as key factors. Social media fatigue emerged as the predominant contributor to lurking.

5.2 Theoretical implications

The research purpose is to explore the determinants and consequences of social media fatigue. Previous studies have explored many of the determinants (e.g., self-disclosure, FoMO, social comparison, privacy concerns, information overload, and system overload) and consequences (e.g., anxiety, depression, and emotional stress) of social media fatigue ( Bright et al., 2015 ; Lee et al., 2016 ; Dhir et al., 2018 ; Logan et al., 2018 ; Kaur et al., 2021 ; Pang, 2021 ; Tandon et al., 2021 ). However, most previous studies have focused on the factors that cause social media fatigue, but the consequences of fatigue are rarely discussed. Also, most of the previous studies discussing the consequences of social media fatigue have been about declines in social media activity, discontinuous use, and discontinuing behaviors ( Luqman et al., 2017 ; Fu et al., 2020 ; Liu Z. et al., 2021 ).

Furthermore, by merging CLT ( Sweller, 2023 ) with social comparison theory ( Festinger, 1954 ; Powdthavee, 2024 ) and social cognitive theory ( Chou et al., 2024 ), the research illustrates how factors like information overload ( Pang, 2021 ) and compulsive social media use ( Dhir et al., 2018 ) lead to cognitive exhaustion and fatigue, emphasizing the unique cognitive strain associated with digital environments. Additionally, it highlights the applicability of social comparison theory ( De Vries et al., 2023 ) by demonstrating how frequent social comparisons and FOMO on social media platforms lead to emotional fatigue ( Jabeen et al., 2023 ). It also signifies the importance of employing social cognitive theory ( Almulla and Al-Rahmi, 2023 ) to indicate the relationship between self-efficacy and social media fatigue ( Liu and He, 2021 ). Identifying social media fatigue as a mediator clarifies the indirect effects of these antecedents on outcomes such as social anxiety and lurking behaviors. Consequently, this further signifies the importance of interventions to manage these cognitive and emotional stressors. The findings promote a comprehensive framework that integrates multiple theoretical perspectives to understand the complex impact of social media on users.

This study is different from previous studies. This study uses lurking as a social media fatigue behavioral consequence, which is discussed in relatively few studies as a research direction. The research results showed a significant positive impact of social media fatigue on lurking and confirmed the relationship between these two factors. The findings contribute to research exploring social media fatigue.

5.3 Practical implications

The research findings have important implications for social media users, managers, and marketers. First, the implications for users. The research results show that compulsive social media use, FoMO, and information overload make users feel fatigued. Social media users should understand that compulsive use comes from their inability to restrain IAD. Also, perceptions of FoMO and information overload can directly impact an individual’s social media fatigue. Therefore, these psychological pressures can lead to fear of expressing oneself online and excessive concern about what others think of them. Second, the implications for operators and providers of social media services. This study proposes negative factors contributing to social media fatigue. Social media fatigue comes not only from human interactions but also from interactions with companies and brands ( Bright et al., 2015 ). The purpose of users using social media is not only to establish contact with others, express personal opinions, and check news and current events but also to entertain and kill time. However, excessive use of the internet and social media leads to social media fatigue, leading to lurking. Hence, this study suggests that social media operators should strengthen the functions of social media, and provide a more concise user interface and skills or knowledge in order to improve users’ successful experience and self-confidence in the process of use, increase motivation for use, and reduce social media fatigue. Finally, the implications for marketers. The research results show that information overload and FoMO are positively related to social media fatigue. Therefore, marketers should check whether releasing too much information to users leads to information overload. Additionally, if the social media service provider can provide users with the priority to view the most interesting and favorite content, it can avoid the user’s fear of missing information, and reduce unwanted content, which can reduce information overload.

5.4 Research limitations and future research suggestions

Although this study took a lot of time and effort, and the process was rigorous, it was still limited by time and resources. This study is described below. First, this study explored social media fatigue without discussing specific social media. The phenomenon of social media fatigue may vary according to the characteristics of different social media or the usage habits of users. Second, this study takes social anxiety as the negative psychological impact of social media fatigue but does not explore whether social anxiety is the specific impact of social media fatigue. Therefore, future research can explore the subsequent behavior of social anxiety on social media. Third, this study adopts a cross-sectional study, which refers to data collection and investigation at a specific time point, and it cannot be confirmed that the long-term results of the study may change over time. Fourth, this study did not consider the influence of personality traits. Thus, future research can explore the characteristics of social media users and the influence of each construct on social media fatigue in more detail. Finally, most of the respondents in this study were between 26 and 45 years old. Respondents of different age groups have different habits of using social media. User experience with social media can vary based on demographics, personality traits, experience, and frequency of use. Therefore, future research can be conducted on various age groups and extend the model to various variables and different cultures or countries.

6 Conclusion

The study’s findings indicate that social media self-efficacy has a negative impact on social media fatigue, whereas compulsive social media use, fear of missing out (FoMO), and information overload have positive impacts. Additionally, social media fatigue is found to significantly contribute to social anxiety and lurking behaviors. These results highlight the crucial mediating role of social media fatigue, offering important insights into how various antecedents affect psychological and behavioral outcomes. This highlights the importance of targeted interventions to reduce cognitive and emotional stress among social media users. Future research should further investigate other mediating and moderating variables to deepen the understanding of these complex relationships and develop strategies that promote healthier social media usage and enhance user well-being.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants or patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

CQ: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing. YiL: Methodology, Writing – original draft, Writing – review & editing. TW: Validation, Writing – original draft, Writing – review & editing. JZ: Methodology, Validation, Writing – original draft, Writing – review & editing. LT: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. JY: Validation, Visualization, Writing – original draft, Writing – review & editing. YuL: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Soft Science Research Project of Henan Province in 2024 (Project name: Research on the Protection, Inheritance and Development of Cultural Space of Traditional Villages in Henan Yanhuang; Project number: 242400411147); Research Project on Integration of Production and Education in Undergraduate Universities in Henan Province (Project name: Comprehensive Reform and Application of Multiple Collaborative Practice Teaching Mode of Design Major under AI Enabling; Project number: 2023348073); Research and Practice Project on Undergraduate Education and Teaching Reform of Henan Agricultural University (Project name: Research and Practice on Teaching Reform of General Courses of Public Art in Colleges and Universities in the New Era of “Educating People with Aesthetics and Infiltrating Integration”; Project number: 2024XJGLX002); Research and Practice Project of Research-based Teaching Reform in Undergraduate Universities (Project name: Writing papers in Geodetic Design, Doing in Hometown: Application Research of Research-based Teaching Mode in Practical Teaching of Design Major; Project number: 2022SYJXLX097).

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.

Publisher’s note

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1277846/full#supplementary-material

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Keywords: social media fatigue, fear of missing out, information overload, social anxiety, lurking

Citation: Qin C, Li Y, Wang T, Zhao J, Tong L, Yang J and Liu Y (2024) Too much social media? Unveiling the effects of determinants in social media fatigue. Front. Psychol . 15:1277846. doi: 10.3389/fpsyg.2024.1277846

Received: 15 August 2023; Accepted: 01 July 2024; Published: 23 July 2024.

Reviewed by:

Copyright © 2024 Qin, Li, Wang, Zhao, Tong, Yang and Liu. 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.

*Correspondence: Ying Li, [email protected] ; Jing Zhao, [email protected]

Disclaimer: 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.

Social Media’s Impact on Self-Esteem

Whether loved or loathed, social media is now an integral part of the communication landscape of contemporary life. As an internet-based communication avenue, social media has earned immense recognition and popularity in the last two decades. The power of this interactive technology has left long-term effects on users. People express varied views and perspectives of these effects. While some tout social media as a digital solution to curb loneliness, significant research efforts and media accounts have confirmed that it undeniably has the opposite impact, especially damaging people’s self-esteem.

There is no doubt that Facebook and other social media platforms have brought substantial benefits to people in modern society. For instance, they allow people to interact effortlessly, have seamless conversations, share content, information, and ideas promptly, communicate electronically, broaden professional knowledge, and create and maintain social connections (Caron & Light, 2015; Luttrell, 2018). However, the impact of these interactive communication technologies on other social life domains such as self-esteem cannot go unnoticed. Researchers have not fully established a direct causative association between social media use and mental health. Even so, the augmented rates of depression, self-victimizations, anxiety, and other mental health issues among young people spending large amounts of time in social networking are outright indications of the damaging effects of social media on these individuals’ self-esteem.

Essentially, scholars have found evidence to affirm a direct causative relationship between social media and low self-esteem. For instance, Bergagna and Tartaglia (2018) empirically explored the relationship between the amount of time people spend on Facebook and self-esteem and found positive correlations. Other research findings by Jan, Soomro, and Ahmad (2017) indicate that augmented social media usage diminishes people’s self-esteem. Using in-depth interviews with women and men aged between 28 and 73, Silva (2017) statistically found out that 60% of active social media users reported that it has damagingly impacted their self-esteem. A more recent study by Jiang and Ngien (2020) corroborated these earlier findings by affirming that spending more time on Instagram and other social media increases predispositions to low self-esteem. The diminished self-esteem culminates in social anxiety, self-victimization, and blaming others. All these research findings are sufficient proof that using Facebook and other social media has detrimental ramifications for people’s self-esteem.

The mechanisms of how Facebook and other social media affect people’s self-esteem adversely are evident. The bedrock of these mechanisms is social comparisons. Scholars and media specialists have demonstrated consensuses that the more time people spend on Instagram, Facebook, and other social media, the more likely they compare themselves socially. In their study, Bergagna and Tartaglia (2018) established that tendencies towards social comparisons directly mediate relationships between self-esteem and the intensity of Facebook usage. Jan, Soomro, and Ahmad (2017) verified that 88% of people using Facebook engross themselves with social comparisons. Statistically, 98% of them are mostly upward social comparisons. The upward comparisons that people make on Facebook and other social networking platforms drive them towards negative self-evaluations that lower their self-esteem.

Laplante (2022) observes that social media usage amplifies unrealistic online social comparisons among users, culminating in diminished self-esteem and augmented social anxiety. This author adds that these social comparisons can be avenues by which people use what others broadcast online to evaluate their skills, achievements, emotions, and personalities. Similarly, Jiang and Ngien (2020) recently observed that a higher intensity of Facebook and social media usage increases tendencies to engage in social comparisons. Consequently, the extrinsic social approval of the self that people derive from comparing themselves with others on social media drives them to low self-esteem characterized by increased social anxiety. From another outlook, Silva (2017) suggests that social networking only broadcasts positive aspects of people’s lives or what the author dubs the highlight reels. When people get these highlight reels and compare their lives against them, the natural reaction will entail reinforcing poor perceptions of their self-images and self-worth. Besides triggering these perceptions, such social comparisons against other individuals’ highlight reels can stimulate depression, anxiety, and psychotic disorders (Jan, Soomro, & Ahmad, 2017; Silva, 2017). Ultimately, such negative self-evaluations and self-disapprovals and the related underlying psychological distress can breed low self-esteem.

In summary, social media use has ramifications for people’s self-esteem. Users of Facebook, Instagram, and other social networking sites tend to make social comparisons of themselves to others. When doing so, users fail to realize that people portray only positive aspects of their lives, creating the illusion that they are doing better than the users. By triggering comparison with others, social media raises users’ doubts about their self-worth and self-images. These negative self-evaluations are some core causes of low self-esteem.

Stereotypes: Sources and Resistance to Change

Discourses about stereotypes are nothing new in mainstream media and research contexts. Stereotypes have persisted for years in the domains of diversity characteristics such as age, gender, ethnicity, race, and sexual orientation. Stereotypes can be positive in that they uphold positive attributions to certain groups. However, they are habitually negative, evinced by characterizations and generalizations that belittle certain target groups. Irrespective of the nature of stereotypes, two interesting questions worth considering relate to the genesis of stereotypes and why they persist or are resistant to change. Responses to these questions are certainly debatable, revealing interesting thoughts about where stereotypes come from and factors underlying their endurance.

Some researchers and media participants have attempted to examine the origins of stereotypes and rendered intriguing findings. For instance, Brink and Nel (2015) investigated the origins, conceptualizations, and definitions of stereotypes within a South African workplace context. When discussing the genesis of stereotypes, these scholars identified multiple epistemological stances elucidating the sources of stereotypes. The first standpoint is that stereotypes can originate from the globalization of mainstream media. Advertisements, movies, and television shows covered in mass media contain an overflow of stereotypes, thus serving as one principal source of stereotypes learnable by people (Brink & Nel, 2015). In many cases, these elements of the media propagate stereotypical images and representations as a daily occurrence. The outcome is prejudice and discrimination towards out-groups, which stimulate emotional and often negative feelings against such groups.

While Brink and Nel (2015) admit that mainstream media is a source of stereotypes, they are keen to acknowledge that contemporary media do not create stereotyping directly. As per these authors, the media play a facilitative role in generating and maintaining stereotypes. This observation could imply that stereotypes do not have a definite source, but rather they emerge as outcomes of a recycling mechanism. Robertson (2020) recently alluded to this notion of recycled stereotypes by affirming that new stereotypes are seldom created. Rather, stereotypes usually become assimilated into society, and subordinate groups recycle or reuse them to describe newly formed subordinate groups.

The second epistemological standpoint regarding the genesis of stereotypes presented by Brink and Nel (2015) is the concept of attribute assignment through learning. Here, individuals in different settings assign traits indirectly learned or acquired from influential agents to certain groups and individuals, leading to stereotyping. For example, parents, who are influential and principal sources of information, can teach and reinforce stereotypical notions and beliefs to their children. These children assign such attributes to certain groups of schoolmates. Thirdly, Brink and Nel (2015) ascribe the genesis of stereotypes to the social learning theory. As per this theory, people learn to stereotype out-groups based on their direct experiences with specific groups or learning from influential members. If not reprimanded for stereotyping out-groups, these persons continue to engage in stereotyping until it becomes a reinforced practice. Eagly, a Northwestern University professor of psychology, uses the social role theory to provide a similar account. She suggests that occupational roles in everyday life can reinforce gender-based stereotyping (Eagly, 2015). Other sources of stereotypes include discrepancies in social role distribution among men and women and historical attributions associated with institutions such as slavery (Hentschel, Heilman, &, Peus, 2019; Taylor et al., 2019). So, stereotypes have many origins.

Three arguments suffice as justifications for why stereotypes are so resistant to change. Firstly, stereotypes persist because those engaging in stereotyping never experience its negative consequences directly and indirectly. Targets of stereotypes are the ones who feel the direct impact of indignities emanating from assumptions about one’s superficial attributes, the associated prejudice and discrimination, and the threat of being confirmed a stereotype (Boso, 2017). Secondly, stereotypes are resistant to change because of perceivers’ information-processing advantage. Rosennab (2019) argues that the human brain is preprogrammed to categorize and confirm one’s understanding of the world for survival. With this preprogramming, people find someone fitting a particular stereotype and confirm the stereotype, making it become deeply entrenched in their minds. Lastly, stereotypes persist because mainstream media reinforces them, Brink and Nel (2015) suggest. As people continue to watch television, movies, ads, and games that propagate stereotyping on mass media, they become accustomed to stereotyping as a social norm. Consequently, doing away with stereotypes and the associated prejudice and discrimination becomes difficult. Ultimately, stereotypes prevail, breeding more social injustices.

In a nutshell, stereotypes come from media globalization, effects of recycling assimilated typecasts, social learning, attribute assignment, and discrepant role distributions. Once they emerge, stereotypes are resistant to change because of brain preprogramming and media reinforcement. Perceivers also propagate stereotypes because they do not feel the consequences intrinsically.

Bergagna, E., & Tartaglia, S. (2018). Self-esteem, social comparison, and Facebook use.  Europe’s Journal of Psychology ,  14 (4), 831. Doi: 10.5964/ejop.v14i4.1592.

Boso, L. A. (2017). Dignity, inequality, and stereotypes.  Washington Law Review, 92  (3), 1119-1183.

Brink, L., & Nel, J. A. (2015). Exploring the meaning and origin of stereotypes amongst South African employees.  SA Journal of Industrial Psychology ,  41 (1), 1-13. Doi: 10.4102/sajip.v41i1.1234.

Caron, J., & Light, J. (2015). “My world has expanded even though I’m stuck at home”: Experiences of individuals with amyotrophic lateral sclerosis who use augmentative and alternative communication and social media.  American Journal of Speech-Language Pathology ,  24 (4), 680-695. Doi: 10.1044/2015_AJSLP-15-0010.

Eagly, A. (2015).  How do stereotypes form and can they be altered? . Institute of Policy Research (IPR). https://www.ipr.northwestern.edu/news/2015/eagly-stereotypes-social-role-theory.html.

Hentschel, T., Heilman, M. E., & Peus, C. V. (2019). The multiple dimensions of gender stereotypes: A current look at men’s and women’s characterizations of others and themselves.  Frontiers in psychology ,  10 , 11. Doi: 10.3389/fpsyg.2019.00011.

Jan, M., Soomro, S., & Ahmad, N. (2017). Impact of social media on self-esteem.  European Scientific Journal ,  13 (23), 329-341. Doi: 10.19044/esj.2017.v13n23p329.

Jiang, S., & Ngien, A. (2020). The effects of Instagram use, social comparison, and self-esteem on social anxiety: A survey study in Singapore.  Social Media+ Society ,  6 (2), 1-10. Doi: 10.1177/2056305120912488.

Laplante, L. (2022). How social media can crush your self-esteem.  The Conversation.  The Conversation Africa, Inc. https://theconversation.com/how-social-media-can-crush-your-self-esteem-174009.

Luttrell, R. (2018).  Social media: How to engage, share, and connect . Rowman & Littlefield.

Robertson, S. (2020). Module 9: Social identities: Race, ethnicity and nationality. In  Foundations in Sociology I . Pressbooks.

Rosennab, M. (2019).  Why stereotypes are hard to resist and how to resist them . Medium. https://medium.com/@rosennab/why-stereotypes-are-hard-to-resist-and-how-to-resist-them-99ca5d820173.

Silva, C. (2017). Social media’s impact on self-esteem.  Huffpost . BuzzFeed, Inc. https://www.huffpost.com/entry/social-medias-impact-on-self-esteem_b_58ade038e4b0d818c4f0a4e4.

Taylor, E., Guy-Walls, P., Wilkerson, P., & Addae, R. (2019). The historical perspectives of stereotypes on African-American males.  Journal of Human Rights and Social Work ,  4 (3), 213-225. Doi: 10.1007/s41134-019-00096-y.

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Is Social Media Giving You Brainrot?

Maybe we *all* should go out and touch grass...

Verywell Mind / Getty Images

What Is ‘Brainrot’?

Brainrot behaviors, who’s most likely affected by brainrot, mental health impact of being chronically online.

• Tips To Minimize Brainrot

I love the Internet but damn has it made things harder for us. Outside of social media ruining our mental health and the bajillion relationship trends erupting on TikTok ( burnt toast theory , delulu thinking , orange peel theory test , the list goes on), we now have brainrot.

Just what is brain rot, you might ask? It's the “He gives golden retriever boyfriend energy” or “I'm doing a hot girl walk this afternoon and then a girl dinner.” AKA when being chronically online bleeds into real life.

Some things used to be for the Internet's eyes and ears only. Not now, though. Nope, our brains are rotting for all that screen time and of course, our mental health is suffering for it. Let me explain.

Wait, what is brainrot again? Basically, it's a new popular culture that describes the effects of being chronically online on our mental health. Funny and self-deprecating, but also truthful. We are so glued to screens, passively feeding our minds with random, useless junk that our brains are rotting.

Doomscrooling and consuming negative news from social media are a few brainrot behaviors. So is talking about memes and excessively using Internet slang.

The main sign, however, is when your online time starts to interfere with activities of daily living, says Dr. Julia Kogan , PsyD, a health psychologist with a background in neuropsychology. For example, if you can't sleep because your eyes are glued to your phone or you're forgoing IRL relationships for Twitter and TikTok, you might be brainrotting.

“Other signs may be the difficulty separating from your phone and the need to constantly check for notifications ,” she says. “Eye strain, headaches, or poor posture from phone use can be another sign that too much time is being spent online without a break.”

We all have brainrotted. It's hard not to when so much of our daily lives revolve around our phones. But some of us are suffering from it more than others, and unfortunately, kids seem to be the biggest demographic since the pandemic.

A 2023 systemic review found that the average screen time (measuring from two hours and more) for children six to 14 has increased significantly from 41.3% to 59.4% before and after January 2020, respectively.

Furthermore, additional research reported that, in the United States, children between the ages of eight and 12 spend an estimated four to six hours a day online whereas teens spend up to nine hours.

While brainrot is a joke, it does highlight a serious problem with cognitive consequences, particularly for kids and teens. Licensed therapist Eli Harwood explains that when adolescents are chronically online, their primary areas of growth for that stage of life also go offline. 

Prevents Social Learning and Increases Loneliness

Harwood says that the entire goal of adolescence is to learn social and emotional competence within peer relationships. Experiences like your first crush or making friends at school. Part of this developmental process should be filled with heaps of awkwardness and vulnerability, which requires in-person interactions. 

She adds that when tweens and teens spend these years behind screens, they miss out on these necessary childhood experiences. Things like making friends, navigating conflict, maintaining relationships, and developing an authentic and confident sense of self.

The replacement of in-person interactions for social media has increased rates of loneliness due to isolation, says Dr. Kogan. Excessive social media use can give a false sense of connection without actual benefits. All of which can make people feel lonelier and more isolated, putting them at greater risk of depression.

Negative Impact on Self-Esteem and Body Image

Social media not only mimics genuine connections but also presents an idealized portrayal of life. Too much media consumption can cause feelings of inadequacy or beliefs that you’re not good enough, which may lead to self-esteem issues, says Dr. Joel “Gator” Warsh , a board-certified pediatrician.

Dr. Kogan adds that filters and the focus on physical appearances and unrealistic beauty standards on social media can make people feel self-conscious and dislike their bodies. All of which can increase the risk of disordered eating, poor body image, and negative self-evaluation.

Furthermore, Harwood says that when adolescents are chronically online, they can be exposed to information they are not yet ready to process. 

“Getting inundated with airbrushed images of models, pornography, bullying, and floods of dopamine from gaming and social media feedback loops does a real number on a young brain,” she says. “Heck, most of the adults I know struggle to stay mentally afloat with these online realities.”

Ain't that the truth?

Increases Risk of Anxiety and Depression

Dr. Kogan explains that overexposure to news on social media or other platforms can also increase the risks of anxiety and depression. This constant exposure can create a perception that the world is dangerous, bad, and harmful, which can further increase anxiety and depression.

Potentially Lead to Addiction

You might not think social media is addicting but it sort of is. Social media is designed to trigger the brain’s reward center. When your brain is constantly or excessively stimulated, your brain develops pathways that look similar to an addiction to drugs or other substances.

Many people turn to time online to avoid dealing with anxiety, depression, and other difficult things in their lives. While mindless scrolling may be a distraction, it does not actually address the issue which can cause mental health symptoms to continue.

Tips To Minimize Brainrot

Brainrot is not a permanent diagnosis (it's not a diagnosis at all actually, lol). But there are ways to minimize it for you or your kids.

Delay Smartphone Use For Children

Harwood recommends parents delay smartphone use for their children. Parents should only give teens aged 16 and older a smartphone. The less access and exposure a teen has, the less likely they are to be chronically online, says Dr. Jonathan Haidt , a social psychologist and expert on the effects of social media use on adolescents.

Set Screen Time Boundaries and Limits

Dr. Warsh suggests parents create clearly stated timeframes for screen use that do not interfere with areas such as school and homework, family meals, or sleep. Designate device-free zones and times like dinner time or family movie night.

Harwood also recommends using parental controls and turning off the Internet an hour before bedtime to ensure everyone is sleeping enough and getting time off tech.

Model Good Behavior

It’s not only children who struggle to stay offline—we all do. Warsh adds that children tend to follow what their parents or adults do. Model a balanced screen time in your own routine and younger ones will notice and mimick your behavior.

Replace Online Time With Screenfree Activities

Swap your phone for physical activity, friend hangouts, or other hobbies. The more joy and fulfillment we receive elsewhere, the less likely we'll spend excessive time on social media.

And less social media is really what we need right now. As the kids say, we *all* should go touch some grass.

Qi, J., Yan, Y., & Yin, H. (2023). Screen time among school-aged children of aged 6–14: A systematic review .  Global Health Research and Policy ,  8 (1), 12. https://doi.org/10.1186/s41256-023-00297-z

The American Academy of Child and Adolescent Psychiatry. Screen time and Children .

By Katharine Chan, MSc, BSc, PMP Katharine is the author of three books (How To Deal With Asian Parents, A Brutally Honest Dating Guide and A Straight Up Guide to a Happy and Healthy Marriage) and the creator of 60 Feelings To Feel: A Journal To Identify Your Emotions. She has over 15 years of experience working in British Columbia's healthcare system.

Impact of Social Media on Self-Esteem

European Scientific Journal, 13(23), 329-341

13 Pages Posted: 5 Sep 2017

Muqaddas Jan

Institute of Business Management (IoBM)

Sanobia Soomro

Iqra University

Nawaz Ahmad

RTS (Research, Trainings, and Solutions); Mehran University of Engineering & Technology

Date Written: August 31, 2017

Social media has gained immense popularity in the last decade and its power has left certain long-lasting effects on people. The upward comparisons made using social networking sites have caused people to have lower self-esteems. In order to test the hypothesis 150 students from institute of business management were surveyed through questionnaires and interviews. This research was limited to the students of IoBM and Facebook, being the most popular social networking site was used as the representative of social media. Correlation and regression model was applied to the data with the help of SPSS statistics to test the relationship between social media and self-esteem. The major findings suggest that approximately 88% people engage in making social comparisons on Facebook and out of the 88%, 98% of the comparisons are upward social comparisons. Further this research proves there that there is a strong relationship between social media and self esteem. Increase in social media usage causes the self-esteem of individuals to decrease. One hour spent on Facebook daily results in a 5.574 decrease in the self-esteem score of an individual.

Keywords: Social media, Self-esteem, Social networking sites

JEL Classification: C12, M10, O35

Suggested Citation: Suggested Citation

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• Published: 18 July 2024

The effect of self-esteem on depressive symptoms among adolescents: the mediating roles of hope and anxiety

• Huang Gu 1   na1 ,
• Panpan Zhang 1   na1 &
• Jingyi Li   ORCID: orcid.org/0009-0003-6376-1798 1  

Humanities and Social Sciences Communications volume  11 , Article number:  932 ( 2024 ) Cite this article

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Previous studies have reported low self-esteem contributes to depressive symptoms among adolescents, but the underlying mechanism remains unclear. The present study aimed to examine the mediating roles of hope and anxiety in the relationship between self-esteem and depressive symptoms. 431 adolescents between 13 and 18 years volunteered to complete a battery of questionnaires that included measures on the variables mentioned above. Results found that hope or anxiety mediated the association between self-esteem and female adolescents’ depression, while only anxiety mediated the association between self-esteem and male adolescents’ depression. Our findings highlight different underlying mechanisms between female and male adolescents. In the prevention and intervention of depressive symptoms, sound programs should be selected according to the gender characteristics of adolescents.

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Introduction.

The National Mental Health Development Report (2019–2020) reports that 24.6% of adolescents in China are diagnosed with depression and 7.4% with severe depression. In recent years, depression has become common in Chinese adolescents. Gijzen et al. ( 2021 ) state that adolescents rather than individuals of other ages are more likely to experience some social-emotional disorders, such as depressive symptoms. Existing studies have found that depressive symptoms are related to suicidal behavior (Gijzen et al. 2021 ; Gili et al. 2019 ; Islam et al. 2021 ; Piqueras et al. 2019 ; Shen and Wang 2023 ). More studies are needed to examine risk factors associated with adolescent depressive symptoms for early prevention and intervention.

According to Robins and Trzesniewski’s view ( 2005 ), adolescence is a turbulent time during which the level of self-esteem of adolescents is dramatically reduced. According to the vulnerability model proposed by Butler et al. ( 1994 ), low self-esteem may be a potential risk of adolescent depression. Adolescents with low self-esteem are unable to evaluate their self-worth correctly which in turn contributes to depression (Orth et al. 2012 ; Rosenberg 1965 ). The assumption has been demonstrated by a body of cross-sectional studies on adolescents (Fiorilli et al. 2019 ; Jiang et al. 2021 ). More importantly, a longitudinal research testifying to the causal relationship between low self-esteem and adolescents’ depression consistently found that low self-esteem contributed to adolescent depression (Zhou et al. 2020 ). Despite previous studies showing that low self-esteem is a potential risk factor for the occurrence of adolescent depression, whether low self-esteem influences adolescent depression through other possible risk factors remains unclear. Inspired by prior research conducted by Cimino et al. ( 2015 ) and Brofenbrennery theory (Ryan 2001 ), the present study hypothesized that low levels of self-esteem, a typical characteristic of adolescents, and other risk factors work together to influence the development of adolescent depressive symptoms. Recently, vulnerability to depression has attracted much attention from scholars and mainly refers to a series of risk factors contributing to the occurrence of depression. The cognitive vulnerability-transactional stress theory states a variety of negative cognitive and coping styles can be regarded as cognitive vulnerability to depression which can make individuals suffer from depressive symptoms in certain situations (Hankin and Abramson 2001 ).

Low hope is a typical cognitive vulnerability to depression. It means that an individual does not have the cognitive belief of successfully achieving goals and the coping capacity to generate sound routes to complete goals (Zhou et al. 2018 ). It has been documented that hope is closely associated with self-esteem (Donald et al. 2019 ; Frankham et al. 2020 ). That is, high self-esteem contributes to the formation of a high sense of hope, on the contrary, low self-esteem will reduce the level of hope. Moreover, adolescents with low hope suffer from more depressive symptoms compared with ones with high hope (Zhang et al. 2019 ). Accordingly, based on the above studies, the present study proposed a hypothesis that low hope increases the risk of depression among adolescents with low self-esteem. Accept for low hope, anxiety is regared as another potential co-occurring risk factor of depression in adolescents with low level of self-esteem. Many quantitative studies have found that self-esteem shows close and negative association with anxiety among adolescents (Berber Çelik and Odacı 2020 ; Thoma et al. 2021 ). Furthermore, anxiety is often accompanied by symptoms of depression. Kwong et al. ( 2021 ) found that the polygenic risk for anxiety is associated with an increasing rate of change in adolescent depression. According to these previous studies, the present study proposed the second hypothesis that anxiety contributes to depression among adolescents with low self-esteem.

Taken together, despite that the relationship between low levels of self-esteem and depression has been testified among adolescents, the underlying mechanism remains unknown. According to Cimino et al. ( 2015 ) viewpoint and Brofenbrennery’s theory (Ryan 2001 ), the present study examined what risk factors increase depression among adolescents which are characterized by low self-esteem. We hypothesized that low hope and anxiety will increase the risk of depression in adolescents with low self-esteem, showing that hope or anxiety may play a mediation role in the association between self-esteem and adolescent depression. Given that previous studies showed that gender was important predictive factor for adolescent depressive symptoms (Bai et al. 2020 ; Hards et al. 2020 ; Osborn et al. 2020 ; Puukko et al. 2020 ; Qi et al. 2020 ; Slavich et al. 2020 ; Zhai et al. 2020 ), an exploratory hypothesis was proposed that hope or anxiety may play different roles in the association between self-esteem and depression, separately for male and female adolescents.

Participants and procedure

Under the approval of the principal of a public middle school, a trained researcher first gathered head teachers together and explained the objective of the online survey to them. Then, head teachers posted the survey link to student groups such as WeChat groups or QQ groups. Given that adolescents have certain literacy ability, each questionnaire instruction was presented in the form of text at the beginning of the questionnaire. Adolescents voluntarily participated in this online survey. Non-participation adolescents were assured that this survey had nothing to do with their grades. Questionnaires were administrated individually. Finally, data were collected from 431 adolescents aged between 13 and 18 years ( M  = 15.73; SD  = 0.89). And 52% were females in this sample. All got compensation for their participation. Before this online survey formally began, written informed consents were provided by adolescents’ guardians.

The Rosenberg Self-Esteem Scale (SES) containing 10 self-report items was used to measure adolescent self-esteem in the present study (Rosenberg 1965 ). Respondents are asked to rate items on a 4-point Likert scale from 1 = strongly disagree to 4 = strongly agree. Total scores were calculated. The higher the score, the higher the self-esteem. Previous studies have shown that the SES has good reliability and validity in Chinese samples (Guo et al. 2018 ; Wang et al. 2020 ). Cronbach’s α for the SES in the present study was 0.85.

The Center for Epidemiologic Studies Depression Scale (CES-D) was used to evaluate adolescent depressive symptoms in the past week (Radloff 1991 ). The CES-D contains 20 items which are rated on a 4-point scale ranging from 0 (never) to 3 (always). Total scores were calculated in the present study. Higher scores indicate a greater frequency of depressive symptoms. The CES-D has good metrological attributes such as reliability and validity in previous research on Chinese samples (Chi et al. 2019 ; Gong et al. 2020 ; Wang et al. 2020 ; Q. Zhou et al. 2018 ). Cronbach’s α for the CES-D in the present study was 0.91.

The Children’s Hope Scale (CHS) is widely used to estimate adolescents’ hopeful thinking containing 6 self-report items. Each item is scored according to a 6-point scale ranging from 1 = none of the time to 6 = all of the time. Total scores ranging from 6 to 36 were calculated in the present study. Higher scores present more hopeful thinking. The CHS had good internal consistency among adolescents with Cronbach’s α coefficient of 0.86 in the present study.

The present study used 20-item Self-rating Anxiety Scale (SAS; (Zung 1971 )), to measure adolescents’ anxiety. The scale has good psychometric attributes such as reliability and validity in Chinese samples (Li et al. 2019 ). Respondents rated items on a 4-point response scale ranging from 1 = a little of the time to 4 = most of the time according to their situation. Total scores for each respondent were created in the present study. Higher scores indicate greater anxiety. The SAS has good internal consistency (Cronbach’s α  = 0.67) in the present study.

Statistical analysis

We first conducted correlation analysis in order to examine the relationship between gender, age, self-esteem, hope, anxiety, and depression. Next, structural equation modeling (SEM) was used to examine different mediation roles of hope and anxiety in the association between self-esteem and depression, separately for male and female adolescents. The constructed model via Mplus V8.3 was tested for fit and was corrected according to the correction index. Based on previous studies (Butler et al. 1994 ; Hu and Bentler 1999 ; Kline and Santor 1999 ), there was a good fit between the constructed models in the present study and empirical data (the constructed model in female adolescents: χ 2  = 324.807, df  = 6, p  < 0.001, RMSEA = 0.000, CFI = 1.000, TLI = 1.012, SRMR = 0.008; the constructed model in male adolescents: χ 2  = 306.754, df  = 6, p  < 0.001; RMSEA = 0.000; CFI = 1.000; TLI = 1.019; SRMR = 0.002). The bootstrap method with 5000 resamples was used to examine the 95% confidence intervals (CIs) in which if the CIs excluded zero, the mediation effects of hope and anxiety were significant at p  < 0.05.

Correlation among the studied variables

As shown in Table 1 , the results of correlation analysis showed that gender was closely associated with self-esteem, hope, anxiety, and depressive symptoms ( r self-esteem  = 0.12, p  < 0.05; r hope  = 0.17, p  < 0.01; r anxiety  = −0.10, p  < 0.05; r depressive symptoms  = −0.11, p  < 0.05). Age was closely associated with depressive symptoms ( r  = 0.12, p  < 0.05). Self-esteem was significantly and positively related to hope ( r  = 0.69, p  < 0.001) while self-esteem was significantly and negatively correlated with depressive symptoms and anxiety ( r depressive symptoms  = −0.64, p  < 0.001; r anxiety  = −0.33, p  < 0.001). Moreover, hope was significantly and negatively linked to depressive symptoms ( r  = −0.50, p  < 0.001). Anxiety was significantly positively related to depressive symptoms ( r  = 0.49, p  < 0.001).

Mediating roles of hope and anxiety in the association between self-esteem and adolescent depressive symptoms.

Mediating roles of hope and anxiety in female adolescents

As illustrated in Table 2 , self-esteem significantly and positively predicted hope ( β  = 1.56, p  < 0.001) while self-esteem significantly and negatively predicted anxiety ( β  = −0.24, p  < 0.001). Furthermore, hope had a significant negative effect on depressive symptoms ( β  = −0.10, p  < 0.01) and anxiety had a significant positive effect on depressive symptoms ( β  = 0.59, p  < 0.001). More importantly, the present study found self-esteem still significantly predicted depressive symptoms when hope and anxiety simultaneously entered the constructed multiple mediation model.

The significance of the indirect effects of hope and anxiety was further examined via the bootstrapping method. Table 3 presents 95% confidence intervals (CIs) of total effect, indirect effects of hope and anxiety, and total indirect effect. The indirect effects of hope and anxiety on the relationship between self-esteem and female adolescents’ depressive symptoms were −0.16 and −0.14, accounting for 25.00 and 21.88% of the total effect, respectively. The total indirect effect was −0.30, accounting for 46.88% of the total effect.

Mediating roles of hope and anxiety in male adolescents

As can be seen in Table 4 , self-esteem significantly predicted male adolescents’ hope ( β  = 1.15, p  < 0.001) while self-esteem significantly predicted male adolescents’ anxiety ( β  = −0.15, p  < 0.01). Anxiety significantly predicted male adolescents’ depressive symptoms ( β  = 0.48, p  < 0.001) while hope failed to predict male adolescents’ depressive symptoms ( β  = −0.04, p  > 0.05). The present study also found that self-esteem still significantly predicted male adolescents’ depressive symptoms while anxiety rather than hope entered the constructed model.

The significance of the indirect effect of anxiety on the relationship between self-esteem and male adolescents’ depressive symptoms was investigated through the bootstrapping method. Table 5 illustrates 95% confidence intervals (CIs) of total effect and indirect effect of anxiety. The indirect effect of anxiety on the relationship between self-esteem and male adolescents’ depressive symptoms was −0.07, accounting for 11.48% total effect.

Our study examined inner mechanisms underlying the association between self-esteem and adolescents’ depressive symptoms. Results showed hope or anxiety partially mediated the negative influence of self-esteem on female adolescents’ depressive symptoms, respectively while anxiety rather than hope played a mediating role in the relationship between self-esteem and male adolescents’ depression. Our study not only highlights the role of self-esteem in adolescents’ depressive symptoms but also reveals different inner mechanisms underlying self-esteem to depression among adolescents of different genders.

Corresponding with previous research, we found that self-esteem significantly predicted adolescent depression. Our result provides evidence for the vulnerability model proposed by Sowislo and Orth ( 2013 ) which states low self-esteem contributes to depressive symptoms. Moreover, we also found that female adolescents versus male adolescents suffered from more depression. The result follows previous literature on adolescents demonstrating gender differences (Lewis et al. 2020 ; Lima et al. 2020 ; Puukko et al. 2020 ; Slavich et al. 2020 ; Thorisdottir et al. 2021 ; Turney 2021 ).

According to the above findings and previous studies, SEM was used to investigate the different roles of hope and anxiety in the relationship between self-esteem and depression in male and female adolescents, separately. The results confirm our suspicion that low self-esteem has a negative influence on depression in male and female adolescents through different intrinsic mechanisms. Specifically, female adolescents with high self-esteem reduced depression via an increase in hope or a decrease in anxiety. However, male adolescents with high self-esteem decreased depression via a decrease in anxiety. The findings indicate that male adolescents are not good at mobilizing internal psychological resources (i.e., hope) to cope with depression.

In addition, we also found that self-esteem affected female adolescents’ depression mainly via hope rather than anxiety, while self-esteem affected male adolescents’ depression via anxiety. These results to some extent have some implications for the precise prevention and intervention of depression among different adolescent populations. Specifically, intervention programs aiming to improving psychological cognitive resilience may be more effective in decreasing the occurrence of depression among female adolescents with low self-esteem. However, intervention programs focusing on decreasing negative emotions (i.e., anxiety) may be more effective in decreasing the risk of depression among male adolescents with low self-esteem to decrease the risk of depression. This inference highlights that mental health educators can set up some special courses according to the developmental characteristics of adolescents of different genders to reduce depression among susceptible adolescents, such as ones with low self-esteem.

Taken together, our results reveal inner mechanisms underlying the relationship between self-esteem and adolescent depression. Specifically, low self-esteem increases the risk of female adolescents’ depression via a decrease in hope or an increase in anxiety. However, low self-esteem contributes to male adolescents’ depression via an increase in anxiety. The present study has some limitations that should be considered. First, given that the cross-sectional design is characterized by the inability to infer causality, more longitudinal studies are needed to replicate the roles of hope and anxiety in the association between self-esteem and adolescent depressive symptoms. Second, the present study recruited adolescents from a public middle school by convenience sampling method. The findings should therefore be generalized with caution. Third, hope and anxiety are regarded as mediators in our study. But it remains unclear if other variables moderate the mediating effects of hope and anxiety on the relationship between self-esteem and adolescent depression.

Data availability

The datasets generated or analyzed during the current study are available in the supplementary files.

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Acknowledgements

The National Social Science Fund of China (grant number 19BSH111) and the Henan Provincial Science and Technology Research Project (grant number 212102310985) supported our study.

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Gu, H., Zhang, P. & Li, J. The effect of self-esteem on depressive symptoms among adolescents: the mediating roles of hope and anxiety. Humanit Soc Sci Commun 11 , 932 (2024). https://doi.org/10.1057/s41599-024-03249-1

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Social media addiction relationship with academic engagement in university students: The mediator role of self-esteem, depression, and anxiety

Affiliations.

• 1 Degree in Clinical Psychology, School of Psychological Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras.
• 2 Department of Developmental Psychology and Education, Faculty of Psychology and Speech Therapy, University of Malaga, 29010, Malaga, Spain.
• 3 Department of Pedagogy and Educational Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras.
• PMID: 38293527
• PMCID: PMC10825341
• DOI: 10.1016/j.heliyon.2024.e24384

This research analyzed how addiction to social media relates to academic engagement in university students, considering the mediating role of self-esteem, symptoms of depression, and anxiety. A quantitative methodology was used with a non-experimental-relational design. A set of questionnaires was applied to a non-probabilistic sample of 412 students enrolled at the National Autonomous University of Honduras. On average, participants use 4.83 different social media platforms at least once a week. Instagram and TikTok users report significantly higher levels of social media addiction, symptoms of depression, and anxiety compared to non-users. Directly, social media addiction does not significantly influence academic engagement scores. However, there are significant indirect inverse effects on academic engagement. Symptoms of depression and self-esteem mediate these effects. Social media addiction increases symptoms of depression, which in turn decreases academic engagement scores. Social media addiction decreases self-esteem, which serves as a variable that significantly increases academic engagement. Overall, findings suggest that social media addiction has a total inverse effect on academic engagement; symptoms of depression and self-esteem mediate this relationship. The implications of these findings are discussed.

Keywords: Academic engagement; Anxiety; Depression; Higher education; Mental health; Self-esteem; Social media.

© 2024 The Authors.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Hypothetical mediation model.

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TikTok has established itself as one of the top online platforms for U.S. teens, while the share of teens who use Facebook has fallen sharply

Table of contents.

• Acknowledgments
• Methodology

Pew Research Center conducted this study to better understand teens’ use of digital devices, social media and other online platforms. For this analysis, we surveyed 1,316 U.S. teens. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos recruited the teens via their parents who were a part of its KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

The trend data in this report comes from a Center survey on the same topic conducted from Sept. 25, 2014, to Oct. 9, 2014, and from Feb. 10, 2015, to March 16, 2015. The survey was fielded by the GfK Group on its KnowledgePanel, which was later acquired by Ipsos .

Here are the  questions used for this report , along with responses, and its  methodology .

For the latest survey data on social media and tech use among teens, see “ Teens, Social Media, and Technology 2023 .” 

The landscape of social media is ever-changing, especially among teens who often are on the leading edge of this space. A new Pew Research Center survey of American teenagers ages 13 to 17 finds TikTok has rocketed in popularity since its North American debut several years ago and now is a top social media platform for teens among the platforms covered in this survey. Some 67% of teens say they ever use TikTok, with 16% of all teens saying they use it almost constantly. Meanwhile, the share of teens who say they use Facebook, a dominant social media platform among teens in the Center’s 2014-15 survey , has plummeted from 71% then to 32% today.

YouTube tops the 2022 teen online landscape among the platforms covered in the Center’s new survey, as it is used by 95% of teens. TikTok is next on the list of platforms that were asked about in this survey (67%), followed by Instagram and Snapchat, which are both used by about six-in-ten teens. After those platforms come Facebook with 32% and smaller shares who use Twitter, Twitch, WhatsApp, Reddit and Tumblr. 1

Changes in the social media landscape since 2014-15 extend beyond TikTok’s rise and Facebook’s fall. Growing shares of teens say they are using Instagram and Snapchat since then. Conversely, Twitter and Tumblr saw declining shares of teens who report using their platforms. And two of the platforms the Center tracked in the earlier survey – Vine and Google+ – no longer exist.

There are some notable demographic differences in teens’ social media choices. For example, teen boys are more likely than teen girls to say they use YouTube, Twitch and Reddit, whereas teen girls are more likely than teen boys to use TikTok, Instagram and Snapchat. In addition, higher shares of Black and Hispanic teens report using TikTok, Instagram, Twitter and WhatsApp compared with White teens. 2

This study also explores the frequency with which teens are on each of the top five online platforms: YouTube, TikTok, Instagram, Snapchat and Facebook. Fully 35% of teens say they are using at least one of them “almost constantly.” Teen TikTok and Snapchat users are particularly engaged with these platforms, followed by teen YouTube users in close pursuit. A quarter of teens who use Snapchat or TikTok say they use these apps almost constantly, and a fifth of teen YouTube users say the same. When looking at teens overall, 19% say they use YouTube almost constantly, 16% say this about TikTok, and 15% about Snapchat.

When reflecting on the amount of time they spend on social media generally, a majority of U.S. teens (55%) say they spend about the right amount of time on these apps and sites, while about a third of teens (36%) say they spend too much time on social media. Just 8% of teens think they spend too little time on these platforms.

Asked about the idea of giving up social media, 54% of teens say it would be at least somewhat hard to give it up, while 46% say it would be at least somewhat easy. Teen girls are more likely than teen boys to express it would be difficult to give up social media (58% vs. 49%). Conversely, a quarter of teen boys say giving up social media would be very easy, while 15% of teen girls say the same. Older teens also say they would have difficulty giving up social media. About six-in-ten teens ages 15 to 17 (58%) say giving up social media would be at least somewhat difficult to do. A smaller share of 13- to 14-year-olds (48%) think this would be difficult.

Beyond just online platforms, the new survey finds that the vast majority of teens have access to digital devices, such as smartphones (95%), desktop or laptop computers (90%) and gaming consoles (80%). And the study shows there has been an uptick in daily teen internet users, from 92% in 2014-15 to 97% today. In addition, the share of teens who say they are online almost constantly has roughly doubled since 2014-15 (46% now and 24% then).

These are some of the findings from an online survey of 1,316 teens conducted by the Pew Research Center from April 14 to May 4, 2022. More details about the findings on adoption and use of digital technologies by teens are covered below.

Smartphones, desktop and laptop computers, and gaming consoles remain widely accessible to teens

Since 2014-15, there has been a 22 percentage point rise in the share of teens who report having access to a smartphone (95% now and 73% then). While teens’ access to smartphones has increased over roughly the past eight years, their access to other digital technologies, such as desktop or laptop computers or gaming consoles, has remained statistically unchanged.

The survey shows there are differences in access to these digital devices for certain groups. For instance, teens ages 15 to 17 (98%) are more likely to have access to a smartphone than their 13- to 14-year-old counterparts (91%). In addition, teen boys are 21 points more likely to say they have access to gaming consoles than teen girls – a pattern that has been reported in prior Center research . 3

Access to computers and gaming consoles also differs by teens’ household income. U.S. teens living in households that make $75,000 or more annually are 12 points more likely to have access to gaming consoles and 15 points more likely to have access to a desktop or laptop computer than teens from households with incomes under $30,000. These gaps in teen computer and gaming console access are consistent with digital divides by household income the Center has observed in previous teen surveys.

While 72% of U.S. teens say they have access to a smartphone, a computer and a gaming console at home, more affluent teens are particularly likely to have access to all three devices. Fully 76% of teens that live in households that make at least $75,000 a year say they have or have access to a smartphone, a gaming console and a desktop or laptop computer, compared with smaller shares of teens from households that make less than $30,000 or teens from households making $30,000 to $74,999 a year who say they have access to all three (60% and 69% of teens, respectively).

Almost all U.S. teens report using the internet daily

The share of teens who say they use the internet about once a day or more has grown slightly since 2014-15. Today, 97% of teens say they use the internet daily, compared with 92% of teens in 2014-15 who said the same.

In addition, the share of teens who say they use the internet almost constantly has gone up: 46% of teens say they use the internet almost constantly, up from only about a quarter (24%) of teenagers who said the same in 2014-15.

Black and Hispanic teens stand out for being on the internet more frequently than White teens. Some 56% of Black teens and 55% of Hispanic teens say they are online almost constantly, compared with 37% of White teens. The difference between Hispanic and White teens on this measure is consistent with previous findings when it comes to frequent internet use .

In addition, older teens are more likely to be online almost constantly. Some 52% of 15- to 17-year-olds say they use the internet almost constantly, while 36% of 13- to 14-year-olds say the same. Another demographic pattern in “almost constant” internet use: 53% of urban teens report being online almost constantly, while somewhat smaller shares of suburban and rural teens say the same (44% and 43%, respectively).

Slight differences are seen among those who say they engage in “almost constant” internet use based on household income. A slightly larger share of teens from households making $30,000 to $74,999 annually report using the internet almost constantly, compared with teens from homes making at least $75,000 (51% and 43%, respectively). Teens who live in households making under $30,000 do not significantly differ from either group.

The social media landscape has shifted

This survey asked whether U.S. teens use 10 specific online platforms: YouTube, TikTok, Instagram, Snapchat, Facebook, Twitter, Twitch, WhatsApp, Reddit and Tumblr.

YouTube stands out as the most common online platform teens use out of the platforms measured, with 95% saying they ever use this site or app. Majorities also say they use TikTok (67%), Instagram (62%) and Snapchat (59%). Instagram and Snapchat use has grown since asked about in 2014-15, when roughly half of teens said they used Instagram (52%) and about four-in-ten said they used Snapchat (41%).

The share of teens using Facebook has declined sharply in the past decade. Today, 32% of teens report ever using Facebook, down 39 points since 2014-15, when 71% said they ever used the platform. Although today’s teens do not use Facebook as extensively as teens in previous years, the platform still enjoys widespread usage among adults, as seen in other recent Center studies .

Other social media platforms have also seen decreases in usage among teens since 2014-15. Some 23% of teens now say they ever use Twitter, compared with 33% in 2014-15. Tumblr has seen a similar decline. While 14% of teens in 2014-15 reported using Tumblr, just 5% of teens today say they use this platform.

The online platforms teens flock to differ slightly based on gender. Teen girls are more likely than teen boys to say they ever use TikTok, Instagram and Snapchat, while boys are more likely to use Twitch and Reddit. Boys also report using YouTube at higher rates than girls, although the vast majority of teens use this platform regardless of gender.

Teens’ use of certain online platforms also differs by race and ethnicity. Black and Hispanic teens are more likely than White teens to say they ever use TikTok, Instagram, Twitter or WhatsApp. Black teens also stand out for being more likely to use TikTok compared with Hispanic teens, while Hispanic teens are more likely than their peers to use WhatsApp.

Older teens are more likely than younger teens to say they use each of the online platforms asked about except for YouTube and WhatsApp. Instagram is an especially notable example, with a majority of teens ages 15 to 17 (73%) saying they ever use Instagram, compared with 45% of teens ages 13 to 14 who say the same (a 28-point gap).

Despite Facebook losing its dominance in the social media world with this new cohort of teens, higher shares of those living in lower- and middle-income households gravitate toward Facebook than their peers who live in more affluent households: 44% of teens living in households earning less than $30,000 a year and 39% of teens from households earning $30,000 to less than $75,000 a year say they ever use Facebook, while 27% of those from households earning $75,000 or more a year say the same. Differences in Facebook use by household income were found in previous Center surveys as well (however the differences by household income were more pronounced in the past).

When it comes to the frequency that teens use the top five platforms the survey looked at, YouTube and TikTok stand out as the platforms teens use most frequently. About three-quarters of teens visit YouTube at least daily, including 19% who report using the site or app almost constantly. A majority of teens (58%) visit TikTok daily, while about half say the same for Snapchat (51%) and Instagram (50%).

Looking within teens who use a given platform, TikTok and Snapchat stand out for having larger shares of teenage users who visit these platforms regularly. Fully 86% of teen TikTok or Snapchat users say they are on that platform daily and a quarter of teen users for both of these platforms say they are on the site or app almost constantly. Somewhat smaller shares of teen YouTube users (20%) and teen Instagram users (16%) say they are on those respective platforms almost constantly (about eight-in-ten teen users are on these platforms daily).

Not only is there a smaller share of teenage Facebook users than there was in 2014-15, teens who do use Facebook are also relatively less frequent users of the platform compared with the other platforms covered in this survey. Just 7% of teen Facebook users say they are on the site or app almost constantly (representing 2% of all teens). Still, about six-in-ten teen Facebook users (57%) visit the platform daily.

Across these five platforms, 35% of all U.S. teens say they are on at least one of them almost constantly. While this is not a comprehensive rundown of all teens who use any kind of online platform almost constantly, this 35% of teens represent a group of relatively heavy platform users and they clearly have different views about their use of social media compared with those who say they use at least one of these platforms, though less often than “almost constantly.” Those findings are covered in a later section.

Larger shares of Black and Hispanic teens say they are on TikTok, YouTube and Instagram almost constantly than White teens. For example, Black and Hispanic teens are roughly five times more likely than White teens to say they are on Instagram almost constantly.

Hispanic teens are more likely to be frequent users of Snapchat than White or Black teens: 23% of Hispanic teens say they use this social media platform almost constantly, while 12% of White teens and 11% of Black teens say the same. There are no racial and ethnic differences in teens’ frequency of Facebook usage.

Overall, Hispanic (47%) and Black teens (45%) are more likely than White teens (26%) to say they use at least one of these five online platforms almost constantly.

Slight majorities of teens see the amount of time they spend on social media as about right and say it would be hard to give up

As social media use has become a common part of many teens’ daily routine, the Center asked U.S. teens how they feel about the amount of time they are spending on social media. A slight majority (55%) say the amount of time they spend of social media is about right, and smaller shares say they spend too much time or too little time on these platforms.

While a majority of teen boys and half of teen girls say they spend about the right amount of time on social media, this sentiment is more common among boys. Teen girls are more likely than their male counterparts to say they spend too much time on social media. In addition, White teens are more likely to see their time using social media as about right compared with Hispanic teens. Black teens do not differ from either group.

This analysis also explored how teens who frequently use these platforms may feel about their time on them and how those feelings may differ from teens who use these sites and apps less frequently. To do this, two groups were constructed. The first group is the 35% of teens who say they use at least one of the five platforms this survey covered – YouTube, TikTok, Instagram, Snapchat or Facebook – almost constantly. The other group consists of teens who say they use these platforms but not as frequently – that is, they use at least one of these five platforms but use them less often than “almost constantly.”

When asked how they feel about the time they spend on social media, 53% of teens who almost constantly use at least one of the platforms say they are on social media too much, while about three-in-ten teens (28%) who use at least one of these platforms but less often say the same.

Teens who are almost constantly online – not just on social media – also stand out for saying they spend too much time on social media: 51% say they are on social media too much. By comparison, 26% of teens who are online several times a day say they are on social media too much.

When reflecting on what it would be like to try to quit social media, teens are somewhat divided whether this would be easy or difficult. Some 54% of U.S. teens say it would be very (18%) or somewhat hard (35%) for them to give up social media. Conversely, 46% of teens say it would be at least somewhat easy for them to give up social media, with a fifth saying it would be very easy.

Teenage girls are slightly more likely to say it would be hard to give up social media than teen boys (58% vs. 49%). A similar gap is seen between older and younger teens, with teens 15 to 17 years old being more likely than 13- and 14-year-olds to say it would be at least somewhat hard to give up social media.

A majority of teens who use at least one of the platforms asked about in the survey “almost constantly” say it would be hard to give up social media, with 32% saying it would be very hard. Smaller shares of teens who use at least one of these online platforms but use them less often say the same.

The teens who think they spend too much time on social media also report they would struggle to step back completely from it. Teens who say they spend too much time on social media are 36 percentage points more likely than teens who see their usage as about right to say giving up social media would be hard (78% vs. 42%). In fact, about three-in-ten teens who say they use social media too much (29%) say it would be very hard for them to give up social media. Conversely, a majority of teens who see their social media usage as about right (58%) say that it would be at least somewhat easy for them to give it up.

• A 2018 Center survey also asked U.S. teens about their technology adoption and usage. Direct comparisons cannot be made across the two surveys due to differences in the ways the surveys were conducted. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩
• A 2018 Center survey also asked U.S teens about their video gaming habits. Direct comparisons cannot be made across the two surveys due to differences in the ways the surveys were conducted. Still, there are common patterns between the two separate surveys; for example, teen boys were more likely to report access to a gaming console or that they play video games than teen girls. ↩

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