the effect of social media on self esteem research paper

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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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Social media and self-esteem

Affiliations.

• 1 Human Development and Media Lab, University of California, One Shields Avenue, 373 Kerr Hall, Davis, CA 95616, USA. Electronic address: [email protected].
• 2 Human Development and Media Lab, University of California, One Shields Avenue, 177 Kerr Hall, Davis, CA 95616, USA.
• 3 Weizenbaum Institute for the Networked Society, University of Potsdam, Hardenbergstraße 32, 10623 Berlin, Germany.
• PMID: 35245885
• DOI: 10.1016/j.copsyc.2022.101304

The relationship between social media and self-esteem is complex, as studies tend to find a mixed pattern of relationships and meta-analyses tend to find small, albeit significant, magnitudes of statistical effects. One explanation is that social media use does not affect self-esteem for the majority of users, while small minorities experience either positive or negative effects, as evidenced by recent research calculating person-specific within-person effects. This suggests that the true relationship between social media use and self-esteem is person-specific and based on individual susceptibilities and uses. In recognition of these advancements, we review recent empirical studies considering differential uses and moderating variables in the social media-self-esteem relationship, and conclude by discussing opportunities for future social media effects research.

Keywords: Self-esteem; Self-reflection; Social comparison; Social feedback; Social media.

Copyright © 2022 Elsevier Ltd. All rights reserved.

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Conflict of interest statement

Conflict of interest statement Nothing declared.

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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

Institute of Business Management (IoBM) ( email )

Plot # 84 Korangi Creek Karachi, Sindh 75190 Pakistan

Iqra University ( email )

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Nawaz Ahmad (Contact Author)

Rts (research, trainings, and solutions) ( email ).

9th Nishat Lane, DHA 6 9th Nishat Lane, DHA 6 Karachi, Sindh 75500 Pakistan 00923009292422 (Phone)

Mehran University of Engineering & Technology ( email )

Jamshoro, 76062 Pakistan +923009292422 (Phone)

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Adolescents’ Social Media Experiences and Their Self-Esteem: A Person-Specific Susceptibility Perspective

Volume 2, Issue 2. DOI: 10.1037/tmb0000037

The aim of this preregistered study was to compare and explain the effects of (a) time spent on social media (SM) and (b) the valence (positivity or negativity) of SM experiences on adolescents’ self-esteem. We conducted a 3-week experience sampling (ESM) study among 300 adolescents (13–16 years; 126 assessments per adolescent; 21,970 assessments in total). Using an N = 1 method of analysis (Dynamic Structural Equation Modeling [DSEM]), we found that the within-person effects of time spent with SM on selfesteem ranged from strongly negative (β = –.31) to moderately positive (β = +.27) across adolescents. Across all ESM observations of the valence of adolescents’ SM experiences, 55% of these experiences were positive, 18% negative, and 27% neutral. Finally, 78% of adolescents experienced a positive within-person effect of the valence of SM experiences on self-esteem (β ≥ +.05), 19% no to a very small effect (–.05 < β < +.05), and 3% a negative effect (β ≤ –.05). These sizeable differences in person-specific effects could be explained by adolescents’ self-esteem level, self-esteem instability, and their tendency to base their self-esteem on peer approval.

Keywords: intensive longitudinal data, differential susceptibility, Instagram, Snapchat, Dynamic Structural Equation Modeling (DSEM)

Author Disclosure: This 3-week experience sampling (ESM) study is part of a larger longitudinal study on the psychosocial consequences of social media use among middle adolescents. It uses data from (a) the second 3-week ESM wave, which was fielded in June 2020 and (b) two biweekly surveys around this ESM wave. The study builds upon an earlier ESM study on social media use and self-esteem by Valkenburg, Beyens, et al. (2021a), which used data from the first 3-week ESM wave, fielded in November/ December 2019. A full overview of all preprints and published papers of the larger project can be found on our project website ( https://www.project-awesome.nl/ publications ).

Data Availability: The anonymous data set on which this article is based is published on Figshare (Valkenburg, Pouwels, et al., 2021).

Conflicts of Interest: The authors declare that there is no conflict of interest.

Funding: 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 M. 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

Acknowledgements: We would like to thank Tim Verbeij and Teun Siebers for their contribution to the data collection of this study.

Open Science Disclosures

The data are available at https://doi.org/10.21942/uva.14095971

The analysis scripts and materials are available at https://osf.io/75k4x/

The preregistered design and sampling plan of the larger project is accessible at https://osf.io/327cx

The preregistration of the hypotheses and analysis plan of the current study is available at https://osf.io/43m7t

Correspondence concerning this article should be addressed to Patti M. Valkenburg, Amsterdam School of Communication Research, University of Amsterdam, Spui 21, Amsterdam 1012 CX, The Netherlands. Email: [email protected]

Acquiring self-esteem, the positive and relatively stable evaluation of the self, is a central developmental task in adolescence. Self-esteem may induce adolescents to try out new things, be open to learning and feedback, take calculated risks, and, by doing so, explore their potential. Self-esteem has been positively linked to a healthy peer attachment ( Gorrese & Ruggieri, 2013 ), life satisfaction ( Proctor et al., 2009 ), and success later in life ( Orth & Robins, 2014 ). In the past decade, over a dozen empirical studies have examined the effects of social media (SM) use on adolescents’ self-esteem (e.g., Barthorpe et al., 2020 ; Cingel & Olsen, 2018 ; Meeus et al., 2019 ; Rodgers et al., 2020 ; Valkenburg, Beyens, et al., 2021a ). In addition, two meta-analyses ( Huang, 2017 ; Liu & Baumeister, 2016 ) have tried to integrate the results of these studies, both yielding very small to small negative pooled associations of SM use with self-esteem ( r = −.04, ns, Huang, 2017 ; r = −.09, p < .01, Liu & Baumeister, 2016 ).

A recent experience sampling (ESM) study, based on an earlier ESM wave among the same sample of adolescents as in the present study (see Method section), has attempted to explain these small pooled associations ( Valkenburg, Beyens, et al., 2021a ). In this ESM study, Valkenburg, Beyens, et al. employed a so-called person-specific, N = 1 method of analysis ( McNeish & Hamaker, 2020 ), which allowed them to investigate the unique effects of time spent on SM on each single adolescent’s self-esteem (i.e., by computing a unique effect size for each adolescent). Their study confirmed the weak overall effect of time spent on SM on self-esteem reported in the meta-analyses. But it also revealed substantial differences in the person-specific effects: Whereas most adolescents were not or hardly affected by their time spent on SM, a small group experienced positive effects, and another small group experienced negative effects on self-esteem. Indeed, these preliminary findings suggest that the weak pooled effects reported in the meta-analyses may have been small because they involve overall, average effects resulting from heterogeneous samples of “nonsusceptibles,” “positive susceptibles,” and “negative susceptibles.”

While promising and insightful, earlier work on the effects of SM use on self-esteem leaves two important gaps that, if filled, could further improve our understanding of this effect. First, many previous studies, including Valkenburg, Beyens, et al. (2021a) , have investigated how time spent on SM could affect adolescents’ self-esteem. It is possible, though, that time spent on SM may be too “neutral” to arrive at a true understanding of the effect of SM use on self-esteem. After all, most self-esteem theories emphasize that it is the valence (the positivity or negativity) rather than the duration of experiences that predict fluctuations in self-esteem. It is assumed that self-esteem surges when we succeed or when others accept us and drops when we fail or when others reject us ( Leary & Baumeister, 2000 ). Therefore, the first aim of the present study was to compare the predictive contribution of (a) adolescents’ time spent on SM and (b) the valence (the positivity or negativity) of their SM experiences.

A second gap in the literature is a lack of understanding of the factors that may explain potential differences in the effects of SM use on self-esteem. Earlier studies have predominantly investigated the moderating role of gender in the SM-self-esteem relation (e.g., Blomfield Neira & Barber, 2014 ; Meeus et al., 2019 ), mostly yielding nonsignificant results. In the present study, we extended these earlier studies with four additional factors that may moderate the SM-self-esteem association: Self-esteem level, self-esteem instability, peer approval contingency (i.e., the extent to which adolescents’ self-esteem depends on peer approval), and physical appearance contingency (i.e., the extent to which their self-esteem depends on their physical appearance, Crocker & Wolfe, 2001 ).

To address the two aims of our study, we employed a preregistered 3-week ESM study among 300 middle adolescents, whom we surveyed six times a day (126 measurement moments per person; 21,970 observations in total). We focused on middle adolescence because this is the period of most significant fluctuations in self-esteem ( Harter, 2012 ). Before the start of our study, we conducted a national survey study among 1,000 middle adolescents ( van Driel et al., 2019 ). This survey identified Instagram, Snapchat, and WhatsApp as the three most popular platforms among this age group. From this survey and a series of pilot interviews, we learned that adolescents typically use these platforms in complementary ways, for example, to present themselves to their broader circle of friends (Instagram, Snapchat), to have fun with their friends (Snapchat), and to exchange more intimate information with close friends or family (WhatsApp). Both these public, asynchronous self-presentations and these private, synchronous exchanges with close friends have been shown to affect adolescents’ self-esteem ( Steinsbekk et al., 2021 ; Valkenburg et al., 2017 ).

To capture adolescents’ person-specific susceptibilities to the effects of time spent on SM and the valence of SM experiences on self-esteem, we employed Dynamic Structural Equation Modeling (DSEM). DSEM combines the strengths of multilevel analysis and Structural Equation Modeling with N = 1 time-series analysis ( Asparouhov et al., 2018 ). The N = 1 time-series part of DSEM enabled us to establish the longitudinal, within-person effects of time spent with SM and the valence of SM experiences on the self-esteem of each single adolescent. And it also allowed us to investigate the between-person differences (i.e., the heterogeneity) in these within-person effects.

Within-person effects indicate to what extent SM use leads to changes in a person’s self-esteem as a result of this person’s SM use. Between-person associations indicate whether persons with high SM use have lower (or higher) self-esteem than persons with low SM use. In other words, within-person analyses compare SM-induced changes in self-esteem in a person with this person’s average self-esteem score (i.e., one’s “true” score, Nesselroade, 1991 , p. 229). Between-person analyses compare the SM-induced self-esteem scores of a person with those of other persons. Within-person methods of analysis are generally better attuned to investigate (social) media effects than between-person methods of analysis. After all, a media effect is an intraindividual change within a person due to the media use of this person ( Valkenburg et al., 2016 ), and such changes can therefore best be investigated with within-person methods of analysis.

The Valence of Social Media Experiences

Even though most empirical studies have examined the associations of time spent on SM with self-esteem, most self-esteem theories emphasize that it is the valence of experiences (rather than their duration) that induces ups and downs in self-esteem ( Crocker & Brummelman, 2018 ; Rosenberg, 1986 ). For example, Leary and Baumeister’s (2000) sociometer theory argues that self-esteem serves as a sociometer that gauges the level of approval or disapproval from one’s social environment. Self-esteem goes up when people succeed or when others accept them, and it drops when they fail or when others reject them. Sociometer theory conceptualizes self-esteem as an affectively laden evaluation of the self, meaning that changes in self-esteem are inextricably connected to changes in affect (e.g., feelings of pride and triumph vs. feelings of guilt and embarrassment).

Self-esteem theories acknowledge sizeable individual differences in how the self-esteem of individuals can be affected. These theories argue that some people may experience significant boosts (or drops) in self-esteem in response to minor positive (or negative) experiences, whereas others may only experience self-esteem fluctuations in case of severe self-relevant experiences ( Crocker & Brummelman, 2018 ; Harter & Whitesell, 2003 ). Such individual differences in susceptibility to experiences is also proposed in the differential susceptibility to media effects model ( Valkenburg & Peter, 2013 ) and other media effects theories that postulate that media users can differ greatly in their susceptibility to the effects of their media experiences (for a review, see Valkenburg et al., 2016 ).

The aim of this study was to contribute to media effects and self-esteem theories by investigating and comparing the effects of time spent on SM and the valence of SM experiences on self-esteem, and to explain these effects. Several earlier studies suggest that the positivity or negativity of social media experiences may be a more important predictor of psychosocial outcomes than time spent on social media (e.g., Orben & Dunbar, 2017 ; Primack et al., 2019 ). However, because virtually all earlier studies included time spent on SM as the predictor of self-esteem (for a review, see Valkenburg, Beyens, et al., 2021a ), for reasons of comparability, we also included time spent on SM in the present study.

Based on the weight of evidence in earlier SM-self-esteem studies, we hypothesized that adolescents who spend more time on SM report lower levels of self-esteem than adolescents who do less so (between-person hypothesis, H1). And on the basis of the result of the earlier ESM study by Valkenburg, Beyens, et al. (2021a) , we hypothesized significant between-person differences in the within-person effects of time spent with SM on self-esteem (H2). Furthermore, we expected that adolescents whose experiences on SM are more positive report a higher self-esteem level than adolescents whose experiences are less positive (between-person hypothesis, H3). Finally, we expected that, overall, the more positive adolescents’ SM experiences were in the previous hour, the stronger their increase in self-esteem would be (within-person hypothesis, H4). Finally, we expected significant between-person heterogeneity in this within-person effect (H5).

Investigating Potential Moderators

Even though a detection of person-specific effects of time spent on SM and the valence of SM experiences on self-esteem is valuable in its own right, it does not answer the question of why self-esteem goes up for some and goes down for others in response to similar levels of time spent on SM and similarly valenced SM experiences. To answer this question, we investigated the moderating roles of (a) gender, (b) self-esteem level, (c) self-esteem instability, (d) peer approval contingency, and (e) appearance contingency. To do so, we assessed, in a first step, the person-specific effects of time spent on SM and the valence of SM experiences on self-esteem, and in a second step we studied whether and how these five potential moderators predict these person-specific effects. Several methodologists have called for such a moderation approach to avoid ecological fallacies in the interpretation of results, that is, deriving conclusions about individuals based on analyses of group data (e.g., Lerner & Lerner, 2019 ). We developed research questions rather than hypotheses to investigate the predictive roles of each of the five moderators because earlier work either did not find any moderating effect on the relationship of adolescents’ SM use and self-esteem (in the case of gender) or did not investigate such effects (in the case of the remaining four moderators).

Five earlier studies have investigated the moderating role of gender in the between-person relation of SM use and self-esteem. Four of these studies found a nonsignificant effect ( Blomfield Neira & Barber, 2014 ; Kelly et al., 2019 ; Košir et al., 2016 ; Meeus et al., 2019 ), while one study found a stronger negative SM-self-esteem relation among girls than boys ( Barthorpe et al., 2020 ). It is conceivable that adolescent boys and girls differ in their susceptibility to the effects of time spent on SM and the valence of SM experiences on self-esteem. Adolescent girls generally display somewhat lower levels of self-esteem than adolescent boys ( Harter, 2012 ), report somewhat more frequent social media use ( Pew Research Center, 2018 ), and are somewhat more sensitive to social influences on their self-esteem ( Meier et al., 2011 ). Therefore, we investigated to what extent the within-person effect of time spent with SM on self-esteem (RQ1a) and that of the valence of SM experiences (RQ1b) would depend on adolescents’ gender.

Self-Esteem Level and Instability

The literature offers two opposite hypotheses that consider the effect of social media use on self-esteem: A rich-get-richer hypothesis assumes that particularly adolescents with a high level of self-esteem experience SM-induced increases in self-esteem, which come on top of the many benefits that these adolescents already experience in their offline lives ( Kraut et al., 2002 ; Valkenburg & Peter, 2011 ). Conversely, a social compensation hypothesis assumes that particularly adolescents with a low level of self-esteem experience SM-induced increases in their self-esteem, which may compensate for the lack of positive experiences in their offline lives ( Kraut et al., 2002 ; Valkenburg & Peter, 2011 ).

While the rich-get-richer and social compensation hypotheses are based solely on self-esteem level (whether it is high or low), self-esteem instability theory ( Kernis, 2005 ) argues that it is not so much self-esteem level but self-esteem instability that may inform hypotheses on the influence of the valence of experiences on self-esteem. For example, Kernis (2005) argues that the individuals with instable high self-esteem do show susceptibility to negative experiences, but those with stable high self-esteem do not. Hence, it is not only self-esteem level, but also self-esteem instability that may explain how adolescents respond to SM. Based on these insights, we investigated whether the within-person effects of time spent on SM (RQ2a) and the valence of SM experiences (RQ2b) on self-esteem depend on adolescents’ self-esteem level and self-esteem instability.

Peer Approval and Physical Appearance Contingencies

Self-esteem contingencies are the unique domains of life that serve as the basis of our self-esteem ( Crocker et al., 2003 ). Having contingent self-esteem is functional for cognitive and psychosocial development. After all, if our self-esteem remains stable, no matter what happens, there is no motivation to learn and develop ( Vonk & Smit, 2012 ). Adolescents do seem to differ, though, in the domains on which their self-esteem is contingent. For example, whereas some adolescents’ self-esteem may particularly depend on academic performance, others may base their self-esteem on peer approval.

Different contexts may also activate different self-esteem contingencies ( Crocker & Brummelman, 2018 ). In the classroom, academic competence is valued, which may activate the academic competence contingency. In SM interactions, peer approval and physical appearance are relevant, so that these self-esteem contingencies may particularly be triggered in the SM context. It is, therefore, conceivable that adolescents who particularly base their self-esteem on peer approval or physical appearance are more susceptible to the effects of time spent on SM and the valence of SM experiences on self-esteem than adolescents who do less so. Therefore, we investigated whether the within-person effects of time spent on SM (RQ3a) and the valence of SM experiences (RQ3b) on self-esteem depend on adolescents’ peer approval and physical appearance contingencies.

This preregistered ( https://osf.io/43m7t ) study was part of a larger longitudinal project on the psychosocial consequences of SM use among middle adolescents (13–16 years) that ran from November 2019 to June 2020 (see our website). This larger project consists of a measurement burst design. Such a design, which was first described by Nesselroade (1991) , combines two types of data: (a) longitudinal data with widely spaced intervals (e.g., months, years) and (b) longitudinal data with closely spaced intervals (e.g., hours, days), which are, for example, obtained via ESM or daily dairy studies. These second types of data are called measurement “bursts” ( Nesselroade, 1991 , p. 235).

In our larger project, adolescents completed 16 biweekly online surveys and two 3-week pre-ESM surveys, and participated in two 3-week ESM bursts, one in November/December 2019 and one in June 2020. The design and sampling plan ( https://osf.io/327cx ) of the larger project was preregistered in August 2019 before recruiting participants and collecting data. The hypotheses and analysis plan ( https://osf.io/43m7t ) for the present study was preregistered in October 2020, before analyzing the data of this study.

The present study was based on data from the second ESM study (June 2020), the second pre-ESM survey, and two biweekly surveys, fielded in the first week and just after the third week of this ESM study. The present study built upon Valkenburg, Beyens, et al. (2021a) , which was based on the first ESM study, in three respects. First, it aimed to replicate Valkenburg, Beyens, et al.’s findings on the associations of time spent using SM with self-esteem, using a different measure of time spent with SM. Second, it extended Valkenburg, Beyens, et al.’s study by including the valence of SM experiences as a comparative predictor of self-esteem. Third, it investigated the role of five moderators that may explain the heterogeneity in the person-specific effects on self-esteem.

Participants

The final sample of this study consisted of 300 adolescents (58% girls; M age = 14.61, SD = .70) from Grades 8 and 9 of a large secondary school in the south of the Netherlands. Adolescents were enrolled in different educational tracks: 37% were in lower prevocational secondary education, 34% in intermediate general secondary education, and 29% in academic preparatory education. Of all adolescents, 97% were born in the Netherlands and 98% identified themselves as Dutch. The sample was a fairly accurate representation of the specific area in the Netherlands in terms of educational level and ethnic background.

A priori power analyses using Monte Carlo simulations ( https://osf.io/ar4vm/ ) indicated that a sample size of 300 participants (with 42 assessments) would be sufficient to reliably detect small within-person and moderator effects with a power of .80 and significance level of .05. The original sample of the larger study consisted of 388 adolescents who provided informed consent themselves. Of these 388 adolescents, 300 participants (77%) who used WhatsApp, Instagram, or Snapchat participated in the second ESM wave.

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. Two weeks before the start of the larger project in November 2019, adolescents participated in a classroom session in which they were informed extensively about the aims and procedures of the study. Two weeks before the start of the second ESM study, adolescents received an online instruction on how to install the ESM software application (Ethica Data) on their phones. At this time, adolescents also completed a pre-ESM survey via the Ethica Data app to investigate their use of different SM platforms.

ESM Surveys

Adolescents received six 2-min surveys per day via their mobile phones (Ehtica Data software). Each survey consisted of 19–32 questions, depending on the moment of the day. All surveys included questions on adolescents’ self-esteem, time spent on SM, and the valence (positivity or negativity) of their SM experiences. Adolescents only received questions about their time spent on Instagram, WhatsApp, and Snapchat if they had indicated in the pre-ESM survey that they used these platforms more than once a week. In total, 293 (98%) adolescents received questions about WhatsApp, 261 adolescents (87%) about Instagram, and 232 (77%) about Snapchat.

Sampling Scheme/Monitoring Plan

Adolescents received a total of 126 ESM surveys (i.e., 21 days * 6 assessments per day) at random time points within fixed intervals. A detailed overview of the notification scheme with the response windows can be found on OSF ( http://osf.io/vkr4u ). We messaged adolescents daily to motivate them to fill out as many ESM surveys as possible and to check whether we could help with any technical issues. If adolescents did not complete an ESM survey within 10 min, they received an automatic reminder via the Ethica Data app. Adolescents received a compensation of €0.30 for each completed ESM survey and an additional compensation of €0.20 if they completed the final (longer) ESM survey of the day. In addition, each day, we organized a lottery, in which four adolescents who had completed all six ESM surveys on the previous day could win €25.

Due to unforeseen technical problems with the Ethica software, 140 ESM surveys (0.37%) of the 37,800 surveys that were sent out were not received by adolescents. As a result, adolescents could have completed a maximum number of 37,660 ESM surveys, of which they actually completed (or partially completed) 21,970 surveys. This resulted in a compliance rate of 58%, which is reasonable ( van Roekel et al., 2019 ). On average, adolescents completed 73.23 ESM surveys ( SD = 34.77).

Online Surveys

On the first day of the current ESM study, adolescents received a link to a 5-min survey. This survey contained, among other instruments, items about the two self-esteem contingencies. A small percentage of adolescents (9%) did not complete this survey within 2 weeks. To prevent missing cases (and thus reduced statistical power), these adolescents were asked to complete the self-esteem contingencies questions as part of the end-of-study survey, which was fielded just after the end of the ESM study. Adolescents received a compensation of €2 for completing each online survey. Moreover, all adolescents who completed the online survey within 2 days participated in an additional lottery in which four of them could win €25.

Self-Esteem (ESM)

Based on research that established the validity of single-item measures of self-esteem (e.g., Robins et al., 2001 ), we measured adolescents’ self-esteem by asking participants “How satisfied about yourself do you feel right now?.” Adolescents answered on a 7-point scale, ranging from 0 ( not at all ) to 6 ( completely ), with 3 ( a little ) as the midpoint. Adolescents’ self-esteem level was inferred from the latent mean of all 126 self-esteem observations. Self-esteem instability was computed by calculating the intraindividual standard deviation of self-esteem across all ESM assessments (cf., Meier et al., 2011 ).

Time Spent on SM (ESM)

We asked adolescents how much time in the past hour they had spent with the three most popular platforms in this age group: WhatsApp, Instagram, and Snapchat. Response options ranged from 0 min to 60 min, with 1-min intervals. Adolescents’ time scores for each of the three platforms were summed. In 2.7% of the observations, the estimated time exceeded 60 min. In accordance with our preregistration, these observations were recoded to 60 min.

Valence of Experiences While Using SM (ESM)

Estimating the valence of social media experiences among middle adolescents is a challenge because middle adolescents often have a hard time understanding certain terms that are commonly used in scales designed for older adolescents or adults. Based on pilot interviews, we learned that a positive experience is an experience they like. If adolescents indicated that they had spent at least 1 min using WhatsApp, Instagram, or Snapchat in the past hour, we asked them: “How much did you like your experience on social media in the past hour?.” Response options ranged from 0 ( not at all ) to 6 ( very much ), with 3 ( a little ) as the midpoint.

Peer Approval and Appearance Contingencies of Self-Esteem (Survey)

To measure the peer approval contingency, we presented adolescents with the following two statements: “I feel more satisfied about myself … (a) when others praise me and (b) when I get a lot of attention from others.” To measure the appearance contingency, we used the following two statements: “I feel more satisfied about myself … (a) when I think I am looking good and (b) when I think I am attractive.” Response options ranged from 0 ( do not agree at all ) to 4 ( completely agree ). Following the suggestion of the editor, we deviated from our preregistration and conducted a Confirmatory Factor Analysis rather than a Principal Component Analysis with two fixed factors. The first factor represented the appearance items (Cronbach’s α = .82; Spearman’s rho = .70), the second factor represented the peer approval items (Cronbach’s α = .63; Spearman’s rho = .40). The intercorrelation of the two subscales was r = .55 ( p < .001). For loadings and fit indices, see OSF Supplemental 1 ( https://osf.io/cn3xd/ ).

Method of Analysis

We employed DSEM for intensive longitudinal data in Mplus Version 8.4 ( Asparouhov et al., 2018 ). Following our preregistration ( https://osf.io/43m7t ) we estimated two separate two-level autoregressive lag-1 models, one for time spent on SM, and one for the valence of SM experiences. A detailed overview of the model specifications and overall model fits can be found in OSF Supplemental 2 ( https://osf.io/knsv6/ ). At the within-person level (Level 1) of both models, we controlled for the autoregressive effect of self-esteem (i.e., self-esteem predicted by self-esteem at the previous measurement). In the first model, we included time spent on SM in the past hour as the time-varying covariate (no hypothesis, Model 1). In the second model, we included the valence of SM experiences in the past hour as the time-varying covariate (H4, Model 2). As suggested by Adachi and Willoughby (2015) , we considered an effect size of β = .05 as the smallest effect size of interest for the within-person effects of time spent with SM and the valence of SM experiences on self-esteem.

At the between-person level (Level 2), we examined correlations between the latent mean of self-esteem and the latent mean of time spent on SM (H1, Model 1) and the latent mean of the valence of SM experiences (H3, Model 2). In addition, we investigated heterogeneity in the within-person effects (i.e., random effects), by specifying the between-person variances around the within-person effects of time spent with SM on self-esteem (H2, Model 1) and the valence of SM experiences on self-esteem (H5, Model 2). Finally, we investigated how each of the five moderators (i.e., gender, self-esteem level, self-esteem instability, and the two self-esteem contingencies) were associated with the person-specific within-person effects (variance around Beta) of time spent with SM (RQ1a, RQ2a, RQ3a; Model 1) and the valence of SM experiences on self-esteem (RQ1b, RQ2b, RQ3b; Model 2).

Descriptives and Correlations

Table 1 presents the number of observations, range, means, standard deviations, within-person, and between-person correlations of all variables in the study. As the Table shows, we replicated the high average level of self-esteem of adolescents that was found by Valkenburg, Beyens, et al. (2021a) . Adolescents spent on average almost 15 min using SM in the hour before each observation. Furthermore, their experiences with SM across the 3 weeks were more positive than negative ( M = 3.77, SD = 1.22, range = 0–6). Across 15,708 ESM observations of the valence of SM experiences, 55% of adolescents’ experiences were positive (≥4), whereas 18% of their experiences were negative (≤2). The remaining 27% of the experiences were scored on the midpoint of the scale (3). Finally (not reported in Table 1 ), the intraclass correlations (ICCs) were .53 for self-esteem, .51 for time spent with SM, and .50 for the valence of SM experiences.

Investigating Hypotheses and Research Questions for Time Spent With SM

The outcomes of the model analyzing the effects of time spent with SM on self-esteem are included in Table 2 . In support of our first hypothesis (H1) and consistent with Valkenburg, Beyens, et al. (2021a) , we found that, overall, adolescents who spent more time on social media had lower levels of self-esteem than adolescents who spent less time on social media (i.e., negative between-person association; β = −.14; p = .01). The overall within -person effect of time spent with SM on self-esteem while controlling for self-esteem at the previous assessment was nonsignificant (β = −.01; p = .08). However, in support of our second hypothesis (H2), we did find significant heterogeneity in this overall within-person effect (random effect = 0.012, p = .000), with N = 1 effect sizes of time spent with SM on self-esteem ranging from β = −.31 to β = +.27 across adolescents. When expressed in percentages, 56% of adolescents experienced no to very small effects of time spent with SM on self-esteem (−.05 < β < .05), whereas 27% experienced negative effects (β ≤ .05), and 18% positive effects (β ≥ .05). These effect sizes are visualized in the bottom left histogram in Figure 1 .

The Ranges of the Person-Specific Effects of Time Spent With Social Media (SM) on Self-Esteem for All Adolescents, and for Those With Low, Average, and High Self-Esteem Level and Self-Esteem Instability Note . The x -axis represents the person-specific effect sizes of time spent with SM on self-esteem (Betas), which ranged from β = −.307 to β = +.266 across all 300 adolescents (see bottom left plot). The upper three plots show the person-specific effects of time spent with SM on self-esteem for adolescents with low (<1 SD of the mean), average (within 1 SD of the mean), and high (>1 SD of the mean) self-esteem levels. Although all three upper plots show sizeable heterogeneity in effects within each subgroup, adolescents with a low self-esteem level tended to experience more positive effects of time spent with SM on self-esteem, whereas adolescents with average or high levels of self-esteem tended to experience more negative within-person effects. The lower three right plots show the person-specific effects of time spent with SM on self-esteem for adolescents with low, average, and high self-esteem instability. Although these plots also show sizeable heterogeneity within these subgroups, adolescents with high self-esteem instability tended to experience more negative effects of time spent using SM on self-esteem, whereas adolescents with average or low self-esteem instability tended to experience more positive effects.

Finally, we examined whether the strength and direction of the person-specific within-person effects of time spent with SM on self-esteem (i.e., Beta) depended on gender (RQ1a), self-esteem level, self-esteem instability (RQ2a), and the peer approval and physical appearance self-esteem contingencies (RQ3a). We found evidence for a moderator effect of self-esteem level (β = −.23, p = .01) and self-esteem instability (β = −.25, p = .01), but not of gender and the two self-esteem contingencies. The two significant moderator effects indicated that, compared to their peers, adolescents with a lower self-esteem level (see top left histogram in Figure 1 ) and a lower self-esteem instability (see bottom second left histogram in Figure 1 ) experienced increases in self-esteem after spending more time with SM. Conversely, more adolescents with a higher self-esteem level (see top right histogram in Figure 1 ) and a higher self-esteem instability (see bottom right histogram Figure 1 ) experienced decreases in self-esteem after spending more time with SM.

Investigating Hypotheses and Research Questions for the Valence of SM Experiences

The outcomes of the model analyzing the effects of the valence of SM experiences on self-esteem are presented in Table 3 . In support of our third hypothesis (H3), we found that, overall, adolescents who had more positive SM experiences (i.e., valence) had higher levels of self-esteem than adolescents with less positive SM experiences (i.e., positive between-person association; β = .57; p = .000). In support of our fourth hypothesis (H4), we also found a significant positive overall within -person effect of the valence of SM experiences on self-esteem (β = .15; p = .000). This finding indicates that adolescents’ self-esteem increased (compared to their average self-esteem level) when their SM experiences in the previous hour were more positive. And finally, in support of our H5, we found significant heterogeneity in this within-person effect (random effect = 0.032, p = .000). The person-specific effects of the valence of SM experiences on self-esteem ranged from β = −.45 to β = +.59. Expressed in percentages, 78% of adolescents experienced a positive effect of the valence of SM experiences on self-esteem (β ≥ .05), while 19% experienced no to a very small effect (−.05 < β < .05), and 3% a negative effect (β ≤ .05). The range of these person-specific effect sizes is visualized in the bottom left-hand histogram in Figure 2 .

The Ranges of the Person-Specific Effects of the Valence of Social Media (SM) Experiences on Self-Esteem for All Adolescents, and for Those With Low, Average, and High Peer Approval Contingency and Self-Esteem Instability Note . The x -axis represents the person-specific effect sizes of the valence of SM experiences on self-esteem (Betas), which ranged from β = −.453 to β = +.588 across all 300 adolescents (see bottom left plot). The upper three plots show the person-specific effects of the valence of SM experiences on self-esteem for adolescents with low (<1 SD of the mean), average (within 1 SD of the mean), and high peer approval contingency (>1 SD of the mean). Although these three plots all show a strong tendency toward positive effects of the valence of their SM experiences, adolescents with a high peer approval contingency tended to experience even stronger positive effects of the valence of their SM experiences on self-esteem than adolescents with average and low levels of peer approval contingency. The three bottom right histograms show the person-specific effects of the valence of SM experiences for adolescents with low, average, and high self-esteem instability. Although all these three plots show trends toward positive effects, adolescents with high self-esteem instability tended to experience even stronger positive effects of positive SM experiences on self-esteem than adolescents in the low and average groups.

Finally, we examined whether the strength and direction of the person-specific within-person effects of the valence of SM experiences on self-esteem (i.e., Beta) depended on gender (RQ1b), self-esteem level, self-esteem instability (RQ2b), and the peer approval and physical appearance self-esteem contingencies (RQ3b). We found evidence for moderator effects of self-esteem instability (β = .35, p = .00) and the peer approval contingency (β = .20, p = .006), but not of gender, self-esteem level, and the physical appearance contingency. The two significant moderator effects indicated that adolescents with a higher self-esteem instability (see bottom right histogram in Figure 2 ), as well as adolescents with a higher peer approval contingency (see top right histogram in Figure 2 ) experienced stronger increases in self-esteem due to positive SM experiences than adolescents with a lower self-esteem instability (see top left histogram in Figure 2 ) and a lower peer approval contingency (see bottom second left histogram Figure 2 ).

Exploratory and Sensitivity Analyses

As preregistered, we conducted a validation check to examine whether findings were robust against outliers and potential untrustworthy answer patterns, which they were (for results, see OSF Supplemental 3, https://osf.io/u7deq/ ). We also conducted analyses with time spent using SM and the valence of SM experiences included as predictors in the same model (instead of in two separate models). This model seemed to be too complex because even after 50,000 iterations it did not converge (see OSF Supplemental 4, https://osf.io/t35hd/ ). Finally, because the null effects for the moderator gender were consistent with the results of earlier studies, we investigated to what extent the person-specific effects of time spent with SM and the valence of SM experiences on self-esteem differed within each of the two gender groups. As the histograms show (see OSF Supplemental 5, https://osf.io/uyxrw/ ), the ranges of the person-specific effect sizes were highly comparable among boys and girls. Finally, following a suggestion of one of the reviewers, we investigated whether the effects of time spent on SM and the valence of SM experiences on self-esteem were moderated by adolescents’ educational level. Results showed no significant moderating effect of educational level for time spent on SM, β = −.12, CI [−.228; .002], and the valence of SM experiences, β = .05, CI [−.105; .204].

The first aim of this ESM study was to investigate and replicate the effect of time spent with SM on adolescents’ self-esteem reported by Valkenburg, Beyens et al. (2021a ), and to compare this effect with that of the valence of adolescents’ SM experiences. Consistent with sociometer theory ( Leary & Baumeister, 2000 ), our results revealed that the valence (the positivity and negativity) of adolescents’ SM experiences was a more important predictor of surges and drops in self-esteem than time spent with SM.

Between-Person Associations of Social Media Use With Self-Esteem

Consistent with H1 and earlier studies (e.g., Rodgers et al., 2020 ; Valkenburg, Beyens, et al., 2021a ), we found a negative between-person relation between time spent on SM and self-esteem ( r = −.14). But in support of H3, we found a strong positive between-person association of the valence of SM experiences with self-esteem ( r = +.57). These opposite relations align with the results of a meta-analysis of Liu and Baumeister (2016) , which found that quantitative measures, such as time spent on SM, resulted in negative between-person relations with self-esteem, while more qualitative measures, such as interactions with friends on SM, yielded positive between-person relations with self-esteem. Our between-person associations suggest that adolescents with lower self-esteem levels than their peers spend more time on SM and have fewer positive experiences on SM.

Within-Person Associations of Social Media Use With Self-Esteem

We also found sizeable differences in the predictive power of time spent on SM and the valence of SM experiences on self-esteem. While the overall within-person effect of time spent on SM was close to zero, which replicated the finding of Valkenburg, Beyens, et al. (2021a) , the within-person effect of the valence of SM experiences was positive. This latter effect meant that, consistent with sociometer theory ( Leary & Baumeister, 2000 ), adolescents’ self-esteem surged after positive SM experiences, whereas it dropped after negative SM experiences.

However, these momentary ups and downs in self-esteem must be seen in light of the balance between positive and negative SM experiences among adolescents. Our results showed that 55% of all observed SM experiences were positively valenced, whereas 18% were negatively valenced, a result that is consistent with earlier studies reporting a sizeable positivity bias in SM interactions (e.g., Primack et al., 2019 ; Reinecke & Trepte, 2014 ; Waterloo et al., 2018 ). However, if adolescents’ experiences were more positive than negative, they also experienced more ups than downs in self-esteem across the 3-week period. And this means, consequently and reassuringly, that the significant positive within-person effect of the valence of adolescents’ SM experiences is more due to the ups in self-esteem after positive SM experiences than to the downs in self-esteem after negative SM experiences.

In support of H2 and H5, time spent with SM and the valence of SM experiences led to sizeable heterogeneity in person-specific effects across adolescents. In accordance with media effects theories ( Valkenburg & Peter, 2013 ) and self-esteem theories ( Harter, 2012 ), adolescents differed substantially in their susceptibility to the effects of SM use on self-esteem. A comparison of Figure 1 and Figure 2 shows that the person-specific effects of time spent with SM on self-esteem centered around the overall within-person effect of β = .01 (ranging from β = −.31 to β = +.27), whereas the person-specific effects of the valence of SM experiences on self-esteem concentrated at the right side of the histogram around the overall within-person effect of β = .15 (ranging from β = −.45 to β = +.59).

Explaining Person-Specific Effects of SM Use on Self-Esteem

The second aim of this study was to examine the moderating role of five moderators of the effects of SM use on self-esteem: gender, self-esteem level and instability, and peer approval and physical appearance contingencies. The explanatory power of these moderators varied considerably. First, consistent with earlier between-person studies (e.g., Meeus et al., 2019 ), gender did not moderate the effects of time spent on SM and those of the valence of SM experiences on self-esteem (RQ1a/b). As OSF Supplemental 5 ( https://osf.io/uyxrw/ ) shows, the person-specific effects of both time spent on SM and the valence of SM experiences ranged just as strongly within the boys’ and girls’ groups as they did across these groups. The lack of moderation by gender found in our and earlier studies is apparently due to the high heterogeneity within each of the gender groups, which may have hampered the detection of differences between these groups.

The differences in person-specific effect sizes could be explained by self-esteem level and self-esteem instability (RQ2a/b). First, adolescents with a lower average self-esteem experienced stronger positive effects of time spent with SM on self-esteem than adolescents with higher average self-esteem. This result may point at a social compensation effect ( Kraut et al., 2002 ), indicating that especially adolescents with a low self-esteem use SM to boost their self-esteem. However, as argued in self-esteem theories, probably due to self-protective processes, hardly any adolescent reports a stable low level of self-esteem ( Kernis, 2003 ; cf., Valkenburg, Beyens, et al., 2021a ), which also applies to the present study. Self-esteem theories also argue that it is not so much self-esteem level but self-esteem instability that may inform hypotheses on the influence of one’s experiences on self-esteem (e.g., Kernis, 2003 ).

Regarding self-esteem instability, we found that especially adolescents with high self-esteem instability tended to experience negative effects of time spent on SM on self-esteem, which is at odds with the social compensation hypothesis. For these adolescents, we also found a stronger positive effect of the valence of SM experiences on self-esteem, which may, at first sight, also be at odds with the social compensation hypothesis. However, this latter result must be seen in light of our finding that adolescents with a high self-esteem instability reported significantly fewer positive experiences on SM than their peers (see Table 1 ). Therefore, for these adolescents, the positive effect of the valence of SM experiences on self-esteem may pertain more to drops in self-esteem as a result of negative experiences on SM than to surges in self-esteem as a result of positive experiences on SM. Finally, since self-esteem instability explained only a part of the effect of the valence of SM experiences on self-esteem, other moderators may have played a role. For example, it is possible that adolescents with high self-esteem instability also more often experienced negative offline situations than their peers, and that these negative offline experiences may have co-affected their self-esteem. In all, self-esteem instability seems to be a critical susceptibility factor to explain the momentary effects of positive and negative SM experiences on self-esteem. This result is in line with recent studies showing that adolescents with high mood instability are more prone to develop depressive symptoms than peers with more stable moods (e.g., Maciejewski et al., 2019 ).

Finally, differences in the person-specific effects of the time spent with SM and the valence of SM experiences on self-esteem could partly be explained by adolescents’ peer approval contingency, but not by their physical appearance contingency (RQ3a/b). First, high peer approval or physical appearance contingency did not result in more sizeable person-specific effects of time spent with SM on self-esteem. However, adolescents who particularly based their self-esteem on peer approval did show more susceptibility to the effects of positively valenced SM experiences on their self-esteem than adolescents who did less so. It is conceivable that these adolescents are particularly focused on SM interactions that, for example, elicit positive feedback from their peers to boost their self-esteem.

Avenues for Future Research

Our study also made a first step in investigating potentially valid moderators to explain differences in the person-specific susceptibilities of SM use on self-esteem. We focused mostly on dispositional moderators, such as self-esteem instability and self-esteem contingencies. However, although important as a first step, both developmental and media effect theories argue that the effects of SM use on self-esteem depend on a unique combination of dispositional, developmental, and social-context factors that may differ from adolescent to adolescent (e.g., Bronfenbrenner, 2005 ; Valkenburg & Peter, 2013 ). Future research may therefore extend our study by focusing on other potential dispositional moderators, such as social comparison orientation or social anxiety, in addition to developmental and social-context moderators, such as the norms in families or peer groups. Although this study clearly showed that each and every adolescent may respond differently to SM use, only by investigating the combined validity of dispositional, developmental, and social-context moderators can we truly understand why SM use leads to positive effects among some adolescents, negative effects among others, and null effects among yet others.

Future research may also extend our findings, for example by including more fine-grained measures of SM use than overall time spent on SM as we did in the present study. Several recent survey and ESM studies have investigated the differential effects of passive and active SM use ( Beyens et al., 2020 ; Escobar-Viera et al., 2018 ). To our knowledge, the passive and active SM use dichotomy was introduced by Burke et al. (2010) , and it sparked dozens of subsequent survey and ESM studies ( Valkenburg, Beyens, et al., 2021b ). However, studies into the effects of active and passive SM use still only focus on time spent on social media, albeit time spent browsing and posting. What we really need are studies to investigate the effects of the specific content of adolescents’ SM interactions on their self-esteem.

Investigating the content of SM interactions in survey studies is difficult if not impossible. But it can be realized by linking survey data to additional data collection methods, such as random screenshots of SM interactions ( Reeves et al., 2021 ) or SM data downloads ( Boeschoten et al., 2020 ). Since 2018, analyzing SM data downloads is a promising new prospect to get access to adolescents’ private and public interactions. According to the European General Data Protection Regulation (GDPR; https://gdpr-info.eu/ ), to which all large SM platforms comply, each platform is legally mandated to provide its European users with their SM archives upon request ( Boeschoten et al., 2020 ). These so-called data download packages offer unprecedented opportunities to get insight in the content of private or semipublic social media interactions and their potential consequences for adolescents’ psychosocial functioning ( Griffioen et al., 2020 ), and, thus, they are an important avenue for future research.

The Important Role of Parents and Educators

Adolescents’ positive SM experiences outweighed their negative SM experiences by a factor of three to one. More importantly, most adolescents (78%) also experienced positive effects of these SM experiences on their self-esteem, whereas only 3% experienced negative effects of their SM experiences. Our results may be reassuring news for parents and educators. However, this positivity bias in SM experiences, along with the predominantly positive effects of these experiences, does not prevent the occasional occurrence of negative experiences, and their resulting negative impact on adolescents’ self-esteem. Such negative experiences, and the subsequent drops in self-esteem, are functional. They help adolescents regulating subsequent self-esteem fluctuations, thereby contributing to the longer-term development of a stable self-esteem ( MacDonald & Leary, 2012 ). Therefore, our results do not give reason to keep adolescents away from SM, which they avidly use to interact with their friends and to experiment with their developing identity ( Nesi et al., 2018 ). However, not only the negative susceptibles (i.e., adolescents who experienced mainly negative effects), but all adolescents need their parents and educators to help them prevent, or cope with negative SM experiences. Helping adolescents prevent or cope with negative feedback, social exclusion, or cyberbullying, and explaining them that the SM world is not as perfect as it often appears, should be essential components of today’s parenting and school-based digital literacy programs.

Supplemental Materials

https://doi.org/10.1037/tmb0000037.supp

Copyright © 2021 The Author(s)

Received february 24 , 2021 revision received may 3 , 2021 accepted may 5, 2021.

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• PMC10476631

The Impact of Social Media on the Mental Health of Adolescents and Young Adults: A Systematic Review

Abderrahman m khalaf.

1 Psychiatry Department, Saudi Commission for Health Specialties, Ministry of Health, Riyadh, SAU

Abdullah A Alubied

Ahmed m khalaf.

2 College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU

Abdallah A Rifaey

3 College of Medicine, Almaarefa University, Riyadh, SAU

Adolescents increasingly find it difficult to picture their lives without social media. Practitioners need to be able to assess risk, and social media may be a new component to consider. Although there is limited empirical evidence to support the claim, the perception of the link between social media and mental health is heavily influenced by teenage and professional perspectives. Privacy concerns, cyberbullying, and bad effects on schooling and mental health are all risks associated with this population's usage of social media. However, ethical social media use can expand opportunities for connection and conversation, as well as boost self-esteem, promote health, and gain access to critical medical information. Despite mounting evidence of social media's negative effects on adolescent mental health, there is still a scarcity of empirical research on how teens comprehend social media, particularly as a body of wisdom, or how they might employ wider modern media discourses to express themselves. Youth use cell phones and other forms of media in large numbers, resulting in chronic sleep loss, which has a negative influence on cognitive ability, school performance, and socio-emotional functioning. According to data from several cross-sectional, longitudinal, and empirical research, smartphone and social media use among teenagers relates to an increase in mental distress, self-harming behaviors, and suicidality. Clinicians can work with young people and their families to reduce the hazards of social media and smartphone usage by using open, nonjudgmental, and developmentally appropriate tactics, including education and practical problem-solving.

Introduction and background

Humans are naturally social species that depend on the companionship of others to thrive in life. Thus, while being socially linked with others helps alleviate stress, worry, and melancholy, a lack of social connection can pose major threats to one's mental health [ 1 ]. Over the past 10 years, the rapid emergence of social networking sites like Facebook, Twitter, Instagram, and others has led to some significant changes in how people connect and communicate (Table 1 ). Over one billion people are currently active users of Facebook, the largest social networking website, and it is anticipated that this number will grow significantly over time, especially in developing countries. Facebook is used for both personal and professional interaction, and its deployment has had a number of positive effects on connectivity, idea sharing, and online learning [ 2 ]. Furthermore, the number of social media users globally in 2019 was 3.484 billion, a 9% increase year on year [ 3 ].

Mental health is represented as a state of well-being in which individuals recognize their potential, successfully navigate daily challenges, perform effectively at work, and make a substantial difference in the lives of others [ 4 ]. There is currently debate over the benefits and drawbacks of social media on mental health [ 5 ]. Social networking is an important part of safeguarding our mental health. Mental health, health behavior, physical health, and mortality risk are all affected by the quantity and quality of social contacts [ 5 ].

Social media use and mental health may be related, and the displaced behavior theory could assist in clarifying why. The displaced behavior hypothesis is a psychology theory that suggests people have limited self-control and, when confronted with a challenging or stressful situation, may engage in behaviors that bring instant gratification but are not in accordance with their long-term objectives [ 6 ]. In addition, when people are unable to deal with stress in a healthy way, they may act out in ways that temporarily make them feel better but ultimately harm their long-term goals and wellness [ 7 , 8 ]. In the 1990s, social psychologist Roy Baumeister initially suggested the displaced behavior theory [ 9 ]. Baumeister suggested that self-control is a limited resource that can be drained over time and that when self-control resources are low, people are more likely to engage in impulsive or self-destructive conduct [ 9 ]. This can lead to a cycle of bad behaviors and outcomes, as individuals may engage in behaviors that bring short respite but eventually add to their stress and difficulties [ 9 ]. According to the hypothetical terms, those who participate in sedentary behaviors, including social media, engage in fewer opportunities for in-person social interaction, both of which have been demonstrated to be protective against mental illnesses [ 10 ]. Social theories, on the other hand, discovered that social media use influences mental health by affecting how people interpret, maintain, and interact with their social network [ 4 ].

Numerous studies on social media's effects have been conducted, and it has been proposed that prolonged use of social media sites like Facebook may be linked to negative manifestations and symptoms of depression, anxiety, and stress [ 11 ]. A distinct and important time in a person's life is adolescence. Additionally, risk factors such as family issues, bullying, and social isolation are readily available at this period, and it is crucial to preserve social and emotional growth. The growth of digital technology has affected numerous areas of adolescent lives. Nowadays, teenagers' use of social media is one of their most apparent characteristics. Being socially connected with other people is a typical phenomenon, whether at home, school, or a social gathering, and adolescents are constantly in touch with their classmates via social media accounts. Adolescents are drawn to social networking sites because they allow them to publish pictures, images, and videos on their platforms. It also allows teens to establish friends, discuss ideas, discover new interests, and try out new kinds of self-expression. Users of these platforms can freely like and comment on posts as well as share them without any restrictions. Teenagers now frequently post insulting remarks on social media platforms. Adolescents frequently engage in trolling for amusement without recognizing the potentially harmful consequences. Trolling on these platforms focuses on body shaming, individual abilities, language, and lifestyle, among other things. The effects that result from trolling might cause anxiety, depressive symptoms, stress, feelings of isolation, and suicidal thoughts. The authors explain the influence of social media on teenage well-being through a review of existing literature and provide intervention and preventative measures at the individual, family, and community levels [ 12 ].

Although there is a "generally correlated" link between teen social media use and depression, certain outcomes have been inconsistent (such as the association between time spent on social media and mental health issues), and the data quality is frequently poor [ 13 ]. Browsing social media could increase your risk of self-harm, loneliness, and empathy loss, according to a number of research studies. Other studies either concluded that there is no harm or that some people, such as those who are socially isolated or marginalized, may benefit from using social media [ 10 ]. Because of the rapid expansion of the technological landscape in recent years, social media has become increasingly important in the lives of young people. Social networking has created both enormous new challenges and interesting new opportunities. Research is beginning to indicate how specific social media interactions may impair young people's mental health [ 14 ]. Teenagers could communicate with one another on social media platforms, as well as produce, like, and share content. In most cases, these individuals are categorized as active users. On the other hand, teens can also use social media in a passive manner by "lurking" and focusing entirely on the content that is posted by others. The difference between active and passive social media usage is sometimes criticized as a false dichotomy because it does not necessarily reveal whether a certain activity is goal-oriented or indicative of procrastination [ 15 ]. However, the text provides no justification for why this distinction is wrong [ 16 ]. For instance, one definition of procrastination is engaging in conversation with other people to put off working on a task that is more important. The goal of seeing the information created by other people, as opposed to participating with those same individuals, may be to keep up with the lives of friends. One of the most important distinctions that can be made between the various sorts is whether the usage is social. When it comes to understanding and evaluating all these different applications of digital technology, there are a lot of obstacles to overcome. Combining all digital acts into a single predictor of pleasure would, from both a philosophical and an empirical one, invariably results in a reduction in accuracy [ 17 ].

Methodology

This systematic review was carried out and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and standard practices in the field. The purpose of this study was to identify studies on the influence of technology, primarily social media, on the psychosocial functioning, health, and well-being of adolescents and young adults.

The MEDLINE bibliographical database, PubMed, Google Scholar, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Scopus were searched between 1 January 2000 and 30 May 2023. Social media AND mental health AND adolescents AND young adults were included in the search strategy (impact or relation or effect or influence).

Two researchers (AK and AR) separately conducted a literature search utilizing the search method and evaluated the inclusion eligibility of the discovered papers based on their titles and abstracts. Then, the full texts of possibly admissible publications were retrieved and evaluated for inclusion. Disagreements among the researchers were resolved by debate and consensus.

The researchers included studies that examined the impact of technology, primarily social media, on the psychosocial functioning, health, and well-being of adolescents and young adults. We only considered English publications, reviews, longitudinal surveys, and cross-sectional studies. We excluded studies that were not written in English, were not comparative, were case reports, did not report the results of interest, or did not list the authors' names. We also found additional articles by looking at the reference lists of the retrieved articles.

Using a uniform form, the two researchers (AK and AA) extracted the data individually and independently. The extracted data include the author, publication year, study design, sample size and age range, outcome measures, and the most important findings or conclusions.

A narrative synthesis of the findings was used to analyze the data, which required summarizing and presenting the results of the included research in a logical and intelligible manner. Each study's key findings or conclusions were summarized in a table.

Study Selection

A thorough search of electronic databases, including PubMed, Embase, and Cochrane Library, was done from 1 January 2000 to 20 May 2023. Initial research revealed 326 potentially relevant studies. After deleting duplicates and screening titles and abstracts, the eligibility of 34 full-text publications was evaluated. A total of 23 papers were removed for a variety of reasons, including non-comparative studies, case reports, and studies that did not report results of interest (Figure ​ (Figure1 1 ).

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

This systematic review identified 11 studies that examined the connection between social media use and depression symptoms in children and adolescents. The research demonstrated a modest but statistically significant association between social media use and depression symptoms. However, this relationship's causality is unclear, and additional study is required to construct explanatory models and hypotheses for inferential studies [ 18 ].

Additional research studied the effects of technology on the psychosocial functioning, health, and well-being of adolescents and young adults. Higher levels of social media usage were connected with worse mental health outcomes [ 19 ], and higher levels of social media use were associated with an increased risk of internalizing and externalizing difficulties among adolescents, especially females [ 20 ]. The use of social media was also connected with body image problems and disordered eating, especially among young women [ 21 ], and social media may be a risk factor for alcohol consumption and associated consequences among adolescents and young adults [ 22 ].

It was discovered that cyberbullying victimization is connected with poorer mental health outcomes in teenagers, including an increased risk of sadness and anxiety [ 23 ]. The use of social media was also connected with more depressive symptoms and excessive reassurance-seeking, but also with greater popularity and perceived social support [ 24 ], as well as appearance comparisons and body image worries, especially among young women [ 25 ]. Children and adolescents' bedtime media device use was substantially related to inadequate sleep quantity, poor sleep quality, and excessive daytime drowsiness [ 26 ].

Online friends can be a significant source of social support, but in-person social support appears to provide greater protection against persecution [ 27 ]. Digital and social media use offers both benefits and risks to the health of children and adolescents, and an individualized family media use plan can help strike a balance between screen time/online time and other activities, set boundaries for accessing content, promote digital literacy, and support open family communication and consistent media use rules (Tables ​ (Tables2, 2 , ​ ,3) 3 ) [ 28 ].

Does Social Media Have a Positive or Negative Impact on Adolescents and Young Adults?

Adults frequently blame the media for the problems that younger generations face, conceptually bundling different behaviors and patterns of use under a single term when it comes to using media to increase acceptance or a feeling of community [ 29 , 30 ]. The effects of social media on mental health are complex, as different goals are served by different behaviors and different outcomes are produced by distinct patterns of use [ 31 ]. The numerous ways that people use digital technology are often disregarded by policymakers and the general public, as they are seen as "generic activities" that do not have any specific impact [ 32 ]. Given this, it is crucial to acknowledge the complex nature of the effects that digital technology has on adolescents' mental health [ 19 ]. This empirical uncertainty is made worse by the fact that there are not many documented metrics of how technology is used. Self-reports are the most commonly used method for measuring technology use, but they can be prone to inaccuracy. This is because self-reports are based on people's own perceptions of their behavior, and these perceptions can be inaccurate [ 33 ]. At best, there is simply a weak correlation between self-reported smartphone usage patterns and levels that have been objectively verified [ 34 , 35 ].

When all different kinds of technological use are lumped together into a single behavioral category, not only does the measurement of that category contribute to a loss of precision, but the category also contributes to a loss of precision. To obtain precision, we need to investigate the repercussions of a wide variety of applications, ideally guided by the findings of scientific research [ 36 ]. The findings of this research have frequently been difficult to interpret, with many of them suggesting that using social media may have a somewhat negative but significantly damaging impact on one's mental health [ 36 ]. There is a growing corpus of research that is attempting to provide a more in-depth understanding of the elements that influence the development of mental health, social interaction, and emotional growth in adolescents [ 20 ].

It is challenging to provide a succinct explanation of the effects that social media has on young people because it makes use of a range of different digital approaches [ 37 , 38 ]. To utilize and respond to social media in either an adaptive or maladaptive manner, it is crucial to first have a solid understanding of personal qualities that some children may be more likely to exhibit than others [ 39 ]. In addition to this, the specific behaviors or experiences on social media that put teenagers in danger need to be recognized.

When a previous study particularly questioned teenagers in the United States, the authors found that 31% of them believe the consequences are predominantly good, 45% believe they are neither positive nor harmful, and 24% believe they are unfavorable [ 21 ]. Teens who considered social media beneficial reported that they were able to interact with friends, learn new things, and meet individuals who shared similar interests because of it. Social media is said to enhance the possibility of (i) bullying, (ii) ignoring face-to-face contact, and (iii) obtaining incorrect beliefs about the lives of other people, according to those who believe the ramifications are serious [ 21 ]. In addition, there is the possibility of avoiding depression and suicide by recognizing the warning signs and making use of the information [ 40 ]. A common topic that comes up in this area of research is the connection that should be made between traditional risks and those that can be encountered online. The concept that the digital age and its effects are too sophisticated, rapidly shifting, or nuanced for us to fully comprehend or properly shepherd young people through is being questioned, which challenges the traditional narrative that is sent to parents [ 41 ]. The last thing that needs to be looked at is potential mediators of the link between social factors and teenage depression and suicidality (for example, gender, age, and the participation of parents) [ 22 ].

The Dangers That Come With Young Adults Utilizing Social Media

The experiences that adolescents have with their peers have a substantial impact on the onset and maintenance of psychopathology in those teenagers. Peer relationships in the world of social media can be more frequent, intense, and rapid than in real life [ 42 ]. Previous research [ 22 ] has identified a few distinct types of peer interactions that can take place online as potential risk factors for mental health. Being the target of cyberbullying, also known as cyber victimization, has been shown to relate to greater rates of self-inflicted damage, suicidal ideation, and a variety of other internalizing and externalizing issues [ 43 ]. Additionally, young people may be put in danger by the peer pressure that can be found on social networking platforms [ 44 ]. This can take the form of being rejected by peers, engaging in online fights, or being involved in drama or conflict [ 45 ]. Peer influence processes may also be amplified among teenagers who spend time online, where they have access to a wider diversity of their peers as well as content that could be damaging to them [ 46 ]. If young people are exposed to information on social media that depicts risky behavior, their likelihood of engaging in such behavior themselves (such as drinking or using other drugs) may increase [ 22 ]. It may be simple to gain access to online materials that deal with self-harm and suicide, which may result in an increase in the risk of self-harm among adolescents who are already at risk [ 22 ]. A recent study found that 14.8% of young people who were admitted to mental hospitals because they posed a risk to others or themselves had viewed internet sites that encouraged suicide in the two weeks leading up to their admission [ 24 ]. The research was conducted on young people who were referred to mental hospitals because they constituted a risk to others or themselves [ 24 ]. They prefer to publish pictures of themselves on social networking sites, which results in a steady flow of messages and pictures that are often and painstakingly modified to present people in a favorable light [ 24 ]. This influences certain young individuals, leading them to begin making unfavorable comparisons between themselves and others, whether about their achievements, their abilities, or their appearance [ 47 , 48 ].

There is a correlation between higher levels of social networking in comparison and depressed symptoms in adolescents, according to studies [ 25 ]. When determining how the use of technology impacts the mental health of adolescents, it is essential to consider the issue of displacement. This refers to the question of what other important activities are being replaced by time spent on social media [ 49 ]. It is a well-established fact that the circadian rhythms of children and adolescents have a substantial bearing on both their physical and mental development.

However, past studies have shown a consistent connection between using a mobile device before bed and poorer sleep quality results [ 50 ]. These results include shorter sleep lengths, decreased sleep quality, and daytime tiredness [ 50 ]. Notably, 36% of adolescents claim they wake up at least once over the course of the night to check their electronic devices, and 40% of adolescents say they use a mobile device within five minutes of going to bed [ 25 ]. Because of this, the impact of social media on the quality of sleep continues to be a substantial risk factor for subsequent mental health disorders in young people, making it an essential topic for the continuation of research in this area [ 44 ].

Most studies that have been conducted to investigate the link between using social media and experiencing depression symptoms have concentrated on how frequently and problematically people use social media [ 4 ]. Most of the research that was taken into consideration for this study found a positive and reciprocal link between the use of social media and feelings of depression and, on occasion, suicidal ideation [ 51 , 52 ]. Additionally, it is unknown to what extent the vulnerability of teenagers and the characteristics of substance use affect this connection [ 52 ]. It is also unknown whether other aspects of the environment, such as differences in cultural norms or the advice and support provided by parents, have any bearing on this connection [ 25 ]. Even if it is probable that moderate use relates to improved self-regulation, it is not apparent whether this is the result of intermediate users having naturally greater self-regulation [ 25 ].

Gains From Social Media

Even though most of the debate on young people and new media has centered on potential issues, the unique features of the social media ecosystem have made it feasible to support adolescent mental health in more ways than ever before [ 39 ]. Among other benefits, using social media may present opportunities for humor and entertainment, identity formation, and creative expression [ 53 ]. More mobile devices than ever before are in the hands of teenagers, and they are using social media at never-before-seen levels [ 27 ]. This may not come as a surprise given how strongly young people are drawn to digital devices and the affordances they offer, as well as their heightened craving for novelty, social acceptance, and affinity [ 27 ]. Teenagers are interacting with digital technology for longer periods of time, so it is critical to comprehend the effects of this usage and use new technologies to promote teens' mental health and well-being rather than hurt it [ 53 ]. Considering the ongoing public discussion, we should instead emphasize that digital technology is neither good nor bad in and of itself [ 27 ].

One of the most well-known benefits of social media is social connection; 81% of students say it boosts their sense of connectedness to others. Connecting with friends and family is usually cited by teenagers as the main benefit of social media, and prior research typically supports the notion that doing so improves people's well-being. Social media can be used to increase acceptance or a feeling of community by providing adolescents with opportunities to connect with others who share their interests, beliefs, and experiences [ 29 ]. Digital media has the potential to improve adolescent mental health in a variety of ways, including cutting-edge applications in medical screening, treatment, and prevention [ 28 ]. In terms of screening, past research has suggested that perusing social media pages for signs of melancholy or drug abuse may be viable. More advanced machine-learning approaches have been created to identify mental disease signs on social media, such as depression, post-traumatic stress disorder, and suicidality. Self-report measures are used in most studies currently conducted on adolescent media intake. It is impossible to draw firm conclusions on whether media use precedes and predicts negative effects on mental health because research has only been conducted once. Adults frequently blame the media for the problems that younger generations face [ 30 ]. Because they are cyclical, media panics should not just be attributed to the novel and the unknown. Teenagers' time management, worldview, and social interactions have quickly and dramatically changed as a result of technology. Social media offers a previously unheard-of opportunity to spread awareness of mental health difficulties, and social media-based health promotion programs have been tested for a range of cognitive and behavioral health conditions. Thanks to social media's instant accessibility, extensive possibilities, and ability to reach remote areas, young people with mental health issues have exciting therapy options [ 54 ]. Preliminary data indicate that youth-focused mental health mobile applications are acceptable, but further research is needed to assess their usefulness and effectiveness. Youth now face new opportunities and problems as a result of the growing significance of digital media in their life. An expanding corpus of research suggests that teenagers' use of social media may have an impact on their mental health. But more research is needed [ 18 ] considering how swiftly the digital media landscape is changing.

Conclusions

In the digital era, people efficiently employ technology; it does not "happen" to them. Studies show that the average kid will not be harmed by using digital technology, but that does not mean there are no situations where it could. In this study, we discovered a connection between social media use and adolescent depression. Since cross-sectional research represents the majority, longitudinal studies are required. The social and personal life of young people is heavily influenced by social media. Based on incomplete and contradictory knowledge on young people and digital technology, professional organizations provide guidance to parents, educators, and institutions. If new technologies are necessary to promote social interaction or develop digital and relational (digitally mediated) skills for growing economies, policies restricting teen access to them may be ineffective. The research on the impact of social media on mental health is still in its early stages, and more research is needed before we can make definitive recommendations for parents, educators, or institutions. Reaching young people during times of need and when assistance is required is crucial for their health. The availability of various friendships and services may improve the well-being of teenagers.

The authors have declared that no competing interests exist.