Social Perception Assignment Books Used:? Chadee, D. (2022). Theories in social psychology (2nd ed.). John Wiley & Sons, Inc. ISBN: 9781119627883. Kassin, S., Fein, S., & Markus,

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The effect of social media on well‑being differs from adolescent to adolescent Ine Beyens 1*, J. Loes Pouwels 1, Irene I. van Driel 1, Loes Keijsers 2 & Patti M. Valkenburg 1

The question whether social media use benefits or undermines adolescents’ well‑being is an important societal concern. Previous empirical studies have mostly established across‑the‑board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well‑being. Rigorous analyses of 2,155 real‑time assessments showed that the association between social media use and affective well‑being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person‑specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example1–5). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars6, 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time8–10.

Person‑specific effects. To our knowledge, four recent studies have investigated within-person associa- tions of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results6, 11–13. Orben and colleagues6 found a small negative reciprocal within-person associa- tion between the time spent using social media and life satisfaction. Likewise, Boers and colleagues12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues11 and Jensen and colleagues13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within- person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adoles- cents’ susceptibility to the effects of social media use on well-being (see14, 15). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderat- ing role of sex to compare the effects of social media on boys versus girls6, 11. However, such a group-differential

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1Amsterdam School of Communication Research, University of Amsterdam, 1001 NG Amsterdam, The Netherlands. 2Department of Developmental Psychology, Tilburg University, 5000 LE Tilburg, The Netherlands. *email: [email protected]

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approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual16. After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents14, 15. In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media17, a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well‑being. Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction6), which refers to adolescents’ cognitive judgment of how satisfied they are with their life18. However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions18) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like concep- tualizations of well-being11–13, that is, adolescents’ average well-being across specific time periods18, there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults19, 20, we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being21, 22, which has high convergent validity, as evi- denced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctua- tions in well-being23, 24. Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way25–27, each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games28. In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram28.

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others”29) and passive social media use (i.e., “consuming information without direct exchanges”29). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being29. Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses (https ://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

Results In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r = 0.69, p < 0.001; Instagram: r = 0.38, p < 0.01; WhatsApp: r = 0.85, p < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the dura- tion of active and passive social media use (overall: r = 0.63, p < 0.001; Instagram: r = 0.37, p < 0.001; WhatsApp: r = 0.57, p < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and

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passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person‑specific effects. The within-person associations of social media use with well- being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2), Instagram use (see Table 3), and WhatsApp use (see Table 4). In a first step we examined the aver- age categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had

Table 1. Means, standard deviations, and zero-order between-person and within-person correlations for well-being and duration of use. Means were calculated at the between-person level and represent the average number of minutes that adolescents had spent using social media in the past hour across assessments during which adolescents had used social media/Instagram/WhatsApp in the past hour. All correlations are based on the assessments during which participants had used social media/Instagram/WhatsApp, either actively or passively. N = 63 for active and passive overall social media use; N = 60 for active and passive Instagram use; N = 63 for active and passive WhatsApp use. *p < .05. **p < .01. ***p < .001.

M (SD) Well-being between-person Well-being within-person

Well-being 5.61 (0.75) – –

Duration of overall active social media use 12.47 (11.49) .06 .09**

Duration of overall passive social media use 19.71 (8.95) .17 .07*

Duration of active Instagram use 5.15 (6.69) − .03 .04

Duration of passive Instagram use 9.39 (4.84) − .07 .03

Duration of active WhatsApp use 5.34 (4.14) .08 .04

Duration of passive WhatsApp use 7.34 (3.69) .01 .09***

Table 2. Within-person associations between adolescents’ overall use of social media and well-being. For investigating the categorical associations, the passive and active use predictors were dummy coded (passive use: 0 = no passive use of social media; 1 = passive use of social media; and active use: 0 = no active use of social media; 1 = active use of social media, respectively). WP = within-person; BP = between-person. All predictors were person-mean centered. Models for the duration of use only include assessments during which participants had used social media, either actively or passively. p-values of the fixed part of the model are two- sided, p-values of the random part of the model are one-sided.

Categorical associations Use versus no use (N participants = 63; N assessments = 2,155)

Dose–response associations Duration of use (N participants = 63; N assessments = 1,474)

Model 1A Fixed effects

Model 1B Random effects

Model 2A Fixed effects

Model 2B Random effects

B (SE) p β B (SE) p B (SE) p β B (SE) p

Fixed part

Intercept 5.60 (.10) < .001 7.63 5.60 (.10) < .001 5.66 (.09) < .001 8.05 5.66 (.09) < .001

Assessment (WP) .09 (.04) .020 .09 .09 (.04) .015 .07 (.04) .094 .07 .06 (.04) .091

Passive use (WP) .14 (.09) .111 .06 .14 (.09) .114 .03 (.03) .270 .04 .03 (.03) .243

Active use (WP) .14 (.08) .096 .05 .15 (.08) .058 .07 (.04) .040 .07 .07 (.04) .053

Random part

σ2 residual (WP) 1.16 (.11) < .001 1.13 (.11) < .001 1.10 (.11) < .001 1.08 (.11) < .001

σ2 between-person (BP) .54 (.08) < .001 .54 (.08) < .001 .49 (.09) < .001 .50 (.09) < .001

σ2 passive use (BP) .11 (.05) .015 < .01 (.01) .333

σ2 active use (BP) .05 (.06) .209 .01 (.01) .221

Model fit

Deviance 6,616.58 6,603.86 4,470.03 4,467.78

AIC 6,628.58 6,619.86 4,482.03 4,483.78

BIC 6,662.63 6,665.26 4,513.80 4,526.14

Chi2 (df) 17.52 (2) < .001 2.62 (2) .270

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Table 3. Within-person associations between adolescents’ use of Instagram and well-being. For investigating the categorical associations, the passive and active use predictors were dummy coded (passive use: 0 = no passive use of Instagram; 1 = passive use of Instagram; and active use: 0 = no active use of Instagram; 1 = active use of Instagram, respectively). WP = within-person; BP = between-person. All predictors were person- mean centered. Models for the duration of use only include assessments during which participants had used Instagram, either actively or passively. p-values of the fixed part of the model are two-sided, p-values of the random part of the model are one-sided.

Categorical associations Use versus no use (N participants = 60; N assessments = 2,112)

Dose–response associations Duration of use (N participants = 60; N assessments = 1,075)

Model 3A Fixed effects

Model 3B Random effects

Model 4A Fixed effects

Model 4B Random effects

B (SE) p β B (SE) p B (SE) p β B (SE) p

Fixed part

Intercept 5.64 (.10) < .001 7.89 5.64 (.10) < .001 5.69 (.10) < .001 7.62 5.69 (.10) < .001

Assessment (WP) .08 (.04) .031 .09 .08 (.04) .033 .05 (.05) .347 .09 .05 (.05) .276

Passive use (WP) .15 (.07) .027 .14 .15 (.07) .041 .03 (.07) .695 .14 .06 (.07) .440

Active use (WP) .14 (.09) .137 .13 .13 (.09) .164 .05 (.06) .428 .06 .01 (.07) .879

Random part

σ2 residual (WP) 1.18 (.11) < .001 1.17 (.10) < .001 1.11 (.12) < .001 1.07 (.12) < .001

σ2 between-person (BP) .54 (.08) < .001 .54 (.08) < .001 .47 (.08) < .001 .47 (.08) < .001

σ2 passive use (BP) .02 (.05) .335 .06 (.04) .068

σ2 active use (BP) .05 (.07) .217 .04 (.04) .142

Model fit

Deviance 6,503.54 6,501.67 3,287.59 3,279.66

AIC 6,515.54 6,517.67 3,299.59 3,295.33

BIC 6,549.48 6,562.91 3,329.47 3,335.17

Chi2 (df) 1.37 (2) .503 19.92 (2) < .001

Table 4. Within-person associations between adolescents’ use of WhatsApp and well-being. For investigating the categorical associations, the passive and active use predictors were dummy coded (passive use: 0 = no passive use of WhatsApp; 1 = passive use of WhatsApp; and active use: 0 = no active use of WhatsApp; 1 = active use of WhatsApp, respectively). WP = within-person; BP = between-person. All predictors were person- mean centered. Models for the duration of use only include assessments during which participants had used WhatsApp, either actively or passively. p-values of the fixed part of the model are two-sided, p-values of the random part of the model are one-sided.

Categorical associations Use versus no use (N participants = 63; N assessments = 2,154)

Dose–response associations Duration of use (N participants = 62; N assessments = 1,179)

Model 5A Fixed effects

Model 5B Random effects

Model 6A Fixed effects

Model 6B Random effects

B (SE) p β B (SE) p B (SE) p β B (SE) p

Fixed part

Intercept 5.60 (.10) < .001 7.62 5.60 (.11) < .001 5.69 (.09) < .001 7.62 5.69 (.09) < .001

Assessment (WP) .08 (.04) .023 .09 .09 (.04) .019 .09 (.04) .049 .09 .09 (.04) .047

Passive use (WP) .16 (.05) .001 .14 .22 (.09) .022 .18 (.05) < .001 .14 .18 (.04) < .001

Active use (WP) .06 (.07) .364 .06 .02 (.09) .810 − .05 (.06) .444 .06 − .05 (.05) .258

Random part

σ2 residual (WP) 1.16 (.11) < .001 1.15 (.11) < .001 1.10 (.11) < .001 1.10 (.12) < .001

σ2 between-person (BP) .54 (.08) < .001 .54 (.08) < .001 .47 (.09) < .001 .47 (.09) < .001

σ2 passive use (BP) .04 (.08) .296 < .01 (.01) .440

σ2 active use (BP) .02 (.05) .360 < .01 (.01) .310

Model fit

Deviance 6,615.24 6,613.47 3,590.86 3,591.40

AIC 6,627.24 6,629.47 3,602.86 3,607.40

BIC 6,661.29 6,674.87 3,633.30 3,647.98

Chi2 (df) 1.14 (2) .566 2.81 (2) .245

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used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within- person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we exam- ined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.. The model with the categorical predictors (see Table 2; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adoles- cent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 < r < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 < r < 0.20; 8.20%), moderate (0.20 < r < 0.30; 22.95%) or even strong positive (r ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 < r < − 0.10; 6.56%) or moderate negative (− 0.30 < r < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use. As shown in Model 3A in Table 3, on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well- being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 < r < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 < r < 0.20; 10.87%) or moderate (0.20 < r < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 < r < − 0.10; 2.17%), moderate (− 0.30 < r < − 0.20; 4.35%), or strong (r ≤ − 0.30; 2.17%) negative association. Figure 1 illustrates these differences in the dose–response associations.

WhatsApp use. As shown in Model 5A in Table 4, just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp mes- sages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent pas- sively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

Discussion This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media17, leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

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By providing insights into each individual’s unique susceptibility, the findings of this study provide an expla- nation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media ef

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