research paper on effect of social media on youth

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• Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

• Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
• J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
• Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
• Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
• Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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• Human behaviour

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.

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Introduction.

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 example 1 , 2 , 3 , 4 , 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 scholars 6 , 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 time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 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 adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 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 moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential 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 individual 16 . 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 adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , 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 satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , 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 adults 19 , 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-being 21 , 22 , which has high convergent validity, as evidenced 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 fluctuations in well-being 23 , 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 way 25 , 26 , 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 games 28 . 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 Instagram 28 .

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-being 29 . 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.

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 duration 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 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 average 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 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 examined 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 adolescent 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.

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

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 messages). 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 passively 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).

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 media 17 , 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.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation 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 effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

Best, P., Manktelow, R. & Taylor, B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child Youth Serv. Rev. 41 , 27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 (2014).

Article   Google Scholar  

James, C. et al. Digital life and youth well-being, social connectedness, empathy, and narcissism. Pediatrics 140 , S71–S75. https://doi.org/10.1542/peds.2016-1758F (2017).

Article   PubMed   Google Scholar  

McCrae, N., Gettings, S. & Purssell, E. Social media and depressive symptoms in childhood and adolescence: A systematic review. Adolesc. Res. Rev. 2 , 315–330. https://doi.org/10.1007/s40894-017-0053-4 (2017).

Sarmiento, I. G. et al. How does social media use relate to adolescents’ internalizing symptoms? Conclusions from a systematic narrative review. Adolesc Res Rev , 1–24, doi:10.1007/s40894-018-0095-2 (2018).

Orben, A. Teenagers, screens and social media: A narrative review of reviews and key studies. Soc. Psychiatry Psychiatr. Epidemiol. https://doi.org/10.1007/s00127-019-01825-4 (2020).

Orben, A., Dienlin, T. & Przybylski, A. K. Social media’s enduring effect on adolescent life satisfaction. Proc. Natl. Acad. Sci. USA 116 , 10226–10228. https://doi.org/10.1073/pnas.1902058116 (2019).

Article   CAS   PubMed   Google Scholar  

Whitlock, J. & Masur, P. K. Disentangling the association of screen time with developmental outcomes and well-being: Problems, challenges, and opportunities. JAMA https://doi.org/10.1001/jamapediatrics.2019.3191 (2019).

Hamaker, E. L. In Handbook of Research Methods for Studying Daily Life (eds Mehl, M. R. & Conner, T. S.) 43–61 (Guilford Press, New York, 2012).

Schmiedek, F. & Dirk, J. In The Encyclopedia of Adulthood and Aging (ed. Krauss Whitbourne, S.) 1–6 (Wiley, 2015).

Keijsers, L. & van Roekel, E. In Reframing Adolescent Research (eds Hendry, L. B. & Kloep, M.) (Routledge, 2018).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Does time spent using social media impact mental health? An eight year longitudinal study. Comput. Hum. Behav. 104 , 106160. https://doi.org/10.1016/j.chb.2019.106160 (2020).

Boers, E., Afzali, M. H., Newton, N. & Conrod, P. Association of screen time and depression in adolescence. JAMA 173 , 853–859. https://doi.org/10.1001/jamapediatrics.2019.1759 (2019).

Jensen, M., George, M. J., Russell, M. R. & Odgers, C. L. Young adolescents’ digital technology use and mental health symptoms: Little evidence of longitudinal or daily linkages. Clin. Psychol. Sci. https://doi.org/10.1177/2167702619859336 (2019).

Valkenburg, P. M. The limited informativeness of meta-analyses of media effects. Perspect. Psychol. Sci. 10 , 680–682. https://doi.org/10.1177/1745691615592237 (2015).

Pearce, L. J. & Field, A. P. The impact of “scary” TV and film on children’s internalizing emotions: A meta-analysis. Hum. Commun.. Res. 42 , 98–121. https://doi.org/10.1111/hcre.12069 (2016).

Howard, M. C. & Hoffman, M. E. Variable-centered, person-centered, and person-specific approaches. Organ. Res. Methods 21 , 846–876. https://doi.org/10.1177/1094428117744021 (2017).

Valkenburg, P. M. & Peter, J. The differential susceptibility to media effects model. J. Commun. 63 , 221–243. https://doi.org/10.1111/jcom.12024 (2013).

Eid, M. & Diener, E. Global judgments of subjective well-being: Situational variability and long-term stability. Soc. Indic. Res. 65 , 245–277. https://doi.org/10.1023/B:SOCI.0000003801.89195.bc (2004).

Kross, E. et al. Facebook use predicts declines in subjective well-being in young adults. PLoS ONE 8 , e69841. https://doi.org/10.1371/journal.pone.0069841 (2013).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Reissmann, A., Hauser, J., Stollberg, E., Kaunzinger, I. & Lange, K. W. The role of loneliness in emerging adults’ everyday use of facebook—An experience sampling approach. Comput. Hum. Behav. 88 , 47–60. https://doi.org/10.1016/j.chb.2018.06.011 (2018).

Rutledge, R. B., Skandali, N., Dayan, P. & Dolan, R. J. A computational and neural model of momentary subjective well-being. Proc. Natl. Acad. Sci. USA 111 , 12252–12257. https://doi.org/10.1073/pnas.1407535111 (2014).

Article   ADS   CAS   PubMed   Google Scholar  

Tov, W. In Handbook of Well-being (eds Diener, E.D. et al. ) (DEF Publishers, 2018).

Harter, S. The Construction of the Self: Developmental and Sociocultural Foundations (Guilford Press, New York, 2012).

Steinberg, L. Adolescence . Vol. 9 (McGraw-Hill, 2011).

Rideout, V. & Fox, S. Digital Health Practices, Social Media Use, and Mental Well-being Among Teens and Young Adults in the US (HopeLab, San Francisco, 2018).

Google Scholar  

Waterloo, S. F., Baumgartner, S. E., Peter, J. & Valkenburg, P. M. Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. New Media Soc. 20 , 1813–1831. https://doi.org/10.1177/1461444817707349 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Rideout, V. & Robb, M. B. Social Media, Social Life: Teens Reveal their Experiences (Common Sense Media, San Fransico, 2018).

van Driel, I. I., Pouwels, J. L., Beyens, I., Keijsers, L. & Valkenburg, P. M. 'Posting, Scrolling, Chatting & Snapping': Youth (14–15) and Social Media in 2019 (Center for Research on Children, Adolescents, and the Media (CcaM), Universiteit van Amsterdam, 2019).

Verduyn, P. et al. Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. J. Exp. Psychol. 144 , 480–488. https://doi.org/10.1037/xge0000057 (2015).

Valkenburg, P. M. & Peter, J. Five challenges for the future of media-effects research. Int. J. Commun. 7 , 197–215 (2013).

Verduyn, P., Ybarra, O., Résibois, M., Jonides, J. & Kross, E. Do social network sites enhance or undermine subjective well-being? A critical review. Soc. Issues Policy Rev. 11 , 274–302. https://doi.org/10.1111/sipr.12033 (2017).

Radovic, A., Gmelin, T., Stein, B. D. & Miller, E. Depressed adolescents’ positive and negative use of social media. J. Adolesc. 55 , 5–15. https://doi.org/10.1016/j.adolescence.2016.12.002 (2017).

Valkenburg, P. M., Peter, J. & Schouten, A. P. Friend networking sites and their relationship to adolescents’ well-being and social self-esteem. Cyberpsychol. Behav. 9 , 584–590. https://doi.org/10.1089/cpb.2006.9.584 (2006).

van Roekel, E., Keijsers, L. & Chung, J. M. A review of current ambulatory assessment studies in adolescent samples and practical recommendations. J. Res. Adolesc. 29 , 560–577. https://doi.org/10.1111/jora.12471 (2019).

van Roekel, E., Scholte, R. H. J., Engels, R. C. M. E., Goossens, L. & Verhagen, M. Loneliness in the daily lives of adolescents: An experience sampling study examining the effects of social contexts. J. Early Adolesc. 35 , 905–930. https://doi.org/10.1177/0272431614547049 (2015).

Neumann, A., van Lier, P. A. C., Frijns, T., Meeus, W. & Koot, H. M. Emotional dynamics in the development of early adolescent psychopathology: A one-year longitudinal Study. J. Abnorm. Child Psychol. 39 , 657–669. https://doi.org/10.1007/s10802-011-9509-3 (2011).

Hox, J., Moerbeek, M. & van de Schoot, R. Multilevel Analysis: Techniques and Applications 3rd edn. (Routledge, London, 2018).

Wang, L. P. & Maxwell, S. E. On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychol. Methods 20 , 63–83. https://doi.org/10.1037/met0000030 (2015).

Satorra, A. & Bentler, P. M. Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika 75 , 243–248. https://doi.org/10.1007/s11336-009-9135-y (2010).

Article   MathSciNet   PubMed   PubMed Central   MATH   Google Scholar  

Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6 , 461–464. https://doi.org/10.1214/aos/1176344136 (1978).

Article   MathSciNet   MATH   Google Scholar  

Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19 , 716–723. https://doi.org/10.1109/TAC.1974.1100705 (1974).

Article   ADS   MathSciNet   MATH   Google Scholar  

Keijsers, L. et al. What drives developmental change in adolescent disclosure and maternal knowledge? Heterogeneity in within-family processes. Dev. Psychol. 52 , 2057–2070. https://doi.org/10.1037/dev0000220 (2016).

R Core Team R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2017).

Muthén, L. K. & Muthén, B. O. Mplus User’s Guide 8th edn. (Muthén & Muthén, Los Angeles, 2017).

Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L. & Valkenburg, P. M. Dataset belonging to Beyens et al. (2020). The effect of social media on well-being differs from adolescent to adolescent. https://doi.org/10.21942/uva.12497990 (2020).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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The effect of social media influencers' on teenagers Behavior: an empirical study using cognitive map technique

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The increase in the use of social media in recent years has enabled users to obtain vast amounts of information from different sources. Unprecedented technological developments are currently enabling social media influencers to build powerful interactivity with their followers. These interactions have, in one way or another, influenced young people's behaviors, attitudes, and choices. Thus, this study contributes to the psychological literature by proposing a new approach for constructing collective cognitive maps to explain the effect of social media influencers' distinctive features on teenagers' behavior. More in depth, this work is an attempt to use cognitive methods to identify adolescents' mental models in the Tunisian context. The findings reveal that the influencers' distinctive features are interconnected. As a result, the influencer's distinctive features are confirmed in one way or another, to the teenagers' behavior. These findings provide important insights and recommendations for different users, including psychologists and academics.

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Introduction

The number of social media users has increased rapidly in the last few years. According to the global ‘State of Digital’ report (2021), the number of social media users reached 4.20 billion, which represents 53% of the world’s total population. This number has risen by more than 13% compared to the last year (2020). In Tunisia, until January 2021 the number of social media users has increased to 8.20 million, which represents 69 percent of the total population, while 97%, are accessed via mobile phones. According to the ALEXA report ( 2021 ), Google.com, Facebook are the most used networks by Tunisian people. Most importantly, 18, 5% of Facebook users are under 13 years old.

In fact, the emphasis on social media has created a consensus among tech companies, leading to the creation of more platforms. Today, the diversity of such platforms has created a new horizon of social media in terms of usage and ideas.

Many people whose careers’ are largely reliant on social media are known as "influencers". More than a profession, for some people, it is even considered as a way of life. Influencers use social media every day to express their opinions and critiques on many topics (like lifestyle, health, beauty) and objects (e.g. brands, services, and products). Accordingly, one of the most important marketing strategies in the market is relying on influencers, which has known as influencer marketing (Audrezet et al., 2020 ; Boerman, 2020 ; Lou & Yuan, 2019 ). In 2017, influencer marketing was considered as the most widespread and trendiest’ communication strategy used by the companies. Therefore, influencers have been considered by many marketing experts as opinion leaders because of their important role in persuading and influencing their followers (De Veirman et al., 2017 ). According to the two-step flow of communication theory, the influencer, as a representative of an organization, is inviting to filter, decode and create messages to match with his particular follower base (Lazarsfeld et al., 1944 ). An influencer is a mediator between consumers and organizations. According to Tarsakoo and Charoensukmongkol ( 2019 ), social media marketing implementation capabilities have a positive effect on customer relationship sustainability. In line with the premise of observational learning theory, influence occurs when the consumers use precedent information and observations shared with them gradually to extend their decision-making by evolving their beliefs, attitudes, and behaviors, (Bandura & Adams, 1977 ). In fact, the consumers are sizeable social networks of followers. In their turn, consumers, especially youth and adolescents, consider influencers as a source of transparency, credibility, and source of personal information from what helps the offered brands to be enlarged through the large social media network (e.g. Jin and Phua, 2014).

Social media influencers play a greater role in controlling and influencing the behavior of the consumer especially young people and teenagers (e.g. Marwick, 2015 ; Sokolova & Kefi, 2020 ). Actually, the use of Smartphone's has become an integral part of the lives of both young people and adolescents. According to Anderson ( 2018 ), 95% of teenagers aged between 13 and 17 own a Smartphone. For young people, the pre-social media era has become something of a blur. This generation has known as Generation Z where its members were born between the nineties and the 2000s. What distinguishes this generation is its extensive use of the Internet at an early age. For them, the social media presents an important part of their social life and since then many thinkers set out to explore the effects of using social media platforms at an early age on adolescents' lives. The excessive use of social media may have an effect on teens' mental health. In fact, adolescence is the interval period between childhood and adulthood. A teenager is not a child to act arbitrarily and is not an adult to make critical decisions. Therefore, young people and teenagers have considered as the most sensitive class of consumers. Teenagers' brain creates many changes that make them more sensitive to the impressions of others, especially the view of their peers (e.g. Elkind, 1967 ; Dacey & Kenny, 1994 ; Arnett, 2000 ). Adolescents' mental changes cause many psychological and cognitive problems. According to Social identity theory, teens appreciate the positive reinforcement they get by being included in a group and dislike the feeling of social rejection (Tajfel, 1972 ). To reinforce their sense of belonging, teens are following influencers on social media (e.g., Loureiro & Sarmento, 2019 ). In line with psychological theories, the attachment theory helps to clarify interpersonal relationships between humans. This theory provides the framework to explain the relationship between adolescents and influencers. Several studies have confirmed that the distinctive feature of social media influencers, including relatedness, autonomy and competence affects the behavior, the psychological situation and the emotional side of the consumers (Deci & Ryan, 2000 ). Does the distinctive feature of social media influencers affect teens' behavior? This kind of questions have become among the most controversial ones (e.g. Djafarova & Rushworth, 2017 ). This problem is still inconclusive, even not addressed in some developing countries like Tunisia. Indeed, it is clear that there are considerable gaps in terms of the academic understanding of what characteristics of social media influencers and their effect on teen behaviors. This problem still arises because the lack of empirical works is investigating in this area.

Therefore, this study contributes to the literature by different ways. First, this paper presents a review of the social media influencers' distinctive features in Tunisian context. This is important because social influencers have been considered as credible and trustworthy sources of information (e.g. Sokolova & Kefi, 2020 ). On the others hand, this study identifies the motivations that teens have for following social influencers. MICS6 Survey (2020) shows a gradual increase in suicide rates among Tunisian children (0–19 years). According to the general delegate for child protection, the phenomenon is in part linked to the intensive use of online games. Understanding the main drivers of social media influence among young Tunisians can help professionals and families guide them. Empirically, this study provides the first investigation of teens’ mental models using the cognitive approach.

The rest of this paper is organized as the following: The second part presents thetheoretical background and research hypotheses. The third part introduces the research methodology. The forth part is reserved to application and results. In the last part, both the conclusion and recommendations are highlighted.

Theoretical background and research hypotheses

Social media influencers' distinctive features.

"Informational social influence" is a concept that has been used in literature by Deutsch & Gerard, 1955 ), and defined as the change in behavior or opinions that happened when people (consumers) are conformed to other people (influencers) because they believe that they have precise and true information (e.g. Djafarova & Rushworth, 2017 , Alotaibi et al., 2019 ). According to (Chahal, 2016 ), there are two kinds of "influencers". The classic ones are the scientists, reporters, lawyers, and all others examples of people who have expert-level knowledge and the new ones are the Social media influencers. Accordingly, social media influencers have many followers that trust them especially on the topics related to their domain of knowledge (e.g. Moore et al., 2018 ). According to the Psychology of Influence perspective, people, often, do not realize that they are influenced because the effect occurs mainly in their subconscious (Pligt & Vliek, 2016 ). When influencers advocate an idea, a service, or a product, they can make a psychological conformity effect on followers through their distinctive features (Colliander, 2019 ; Jahoda, 1959 ).

Vollenbroek et al. ( 2014 ) investigated a study about social media influencers and the impact of these actors on the corporate reputation. To create their model, the authors use the Delphi method. The experts have exposed to a questionnaire that included the characteristics of influential actors, interactions, and networks. The first round of research indicates that a bulk of experts has highlighted the importance of intrinsic characteristics of influencers such as knowledge, commitment, and trust etcetera. While others believe that, the size of the network or the reach of a message determines the influence. The results of the second round indicate that the most agreed-upon distinctive characteristics to be a great influencer are being an active mind, being credible, having expertise, being authoritative, being a trendsetter, and having a substantive influence in discussions and conversations. According to previous literature, among the characteristics that distinguish the influencers is the ability to be creative, original, and unique. Recently, Casaló et al. ( 2020 ) indicated that originality and uniqueness positively influence opinion leadership on Instagram. For the rest of this section, we are going to base on the last two studies to draw on the most important distinctive features of social media influencers.

Credibility (expertise and trustworthiness)

According to Lou and Yuan ( 2019 ), one of the most distinctive characteristics that attract the audience is the influencer's credibility specifically the expertise and trustworthiness. In fact, source credibility is a good way of persuasion because it has related to many conceptualizations. Following Hovland et al. ( 1953 ), credibility has subdivided into expertise and trustworthiness. The expertise has reflected the knowledge and competence of the source (influencer) in a specific area (Ki & Kim, 2019 ; McCroskey, 1966 ). While trustworthiness is represented in influencer honesty and sincerity (Giffin, 1967 ). Such characteristics help the source (influencer) to be more convincing. According to the source credibility theory, consumers (social media audience) give more importance to the source of information to take advantage of the expertise and knowledge of influencers (e.g. Ohanian, 1990 ; Teng et al., 2014 ). Spry et al., ( 2011 ) pointed out that a trusted influencer's positive perception of a product and/or service positively affects consumers' attitudes towards recommended brandsHowever, if the product does not meet the required specifications, consumers lose trust in the product and the influencer (Cheung et al., 2009 ). Based on source credibility theory, this work tested one of the research goals: the effect of expertise and credibility on adolescent behavior.

Originality and creativity

Originality in social media represents the ability of an influencer to provide periodically new and differentiate content that attracts the attention of the audience. The content has perceived as innovative, sophisticated, and unusual. Social media influencers look for creating an authentic image in order to construct their own online identity. Marwick ( 2013 ) defined authenticity as "the way in which individuals distinguish themselves, not only from each other but from other types of media". Most of the time, an authentic and different content attracts attention, and sometimes the unusual topics make surprising (Derbaix & Vanhamme, 2003 ). According to Khamis et al. ( 2017 ), social media influencers attract the consumers' attention by posting authentic content. In fact, the audience often appreciates the originality and the creativity of the ideas (Djafarova & Rushworth, 2017 ).The originality of the content posted by an influencer has considered as a way to resonate with their public (Hashoff, 2017 ). When a company seeks to promote its products and services through social media, it is looking for an influential representative who excels at presenting original and different content. The brand needs to be presented by credible and believable influencers that create authentic content (Sireni, 2020 ). One of the aims of this work is to identify the effect of the authentic content on teen’s behaviors.

Trendsetter and uniqueness

According to Maslach et al. ( 1985 ), uniqueness is the case in which the individual feels distinguished compared to others. Tian et al. ( 2001 ) admitted that individuals attempt to be radically different from others to enhance their selves and social images. The uniqueness in content represents the ability of the influencer to provide an uncirculated content specific to him. Gentina et al. ( 2014 ) proved that male adolescents take into account the uniqueness of the content when they evaluated the influencer role particularly in evaluating the role of an opinion leader. Casaló et al. ( 2020 ) indicated that uniqueness positively influences the leadership opinion. Thus, the uniqueness of influencers’ contents may affect audiences’ attitude. Therefore, we aim to test the effect of the influencers’ contents uniqueness and trendsetter on teenagers’ behaviors.

Persuasion has a substantive influence in discussions and conversations. According to the Psychology of Persuasion, the psychological tactic that revolves around harnessing the principles of persuasion supports in one way or another the influencer’s marketing. The objective is to persuade people to make purchase decisions. Persuasion aims commonly to change others attitudes and behavior in a context of relative freedom (e.g. Perloff, 2008 ; Crano & Prislin, 2011 ; Shen & Bigsb, 2013 ). According to Scheer and Stern ( 1992 ), the dynamic effect of marketing occurs when an influencer persuades consumers to participate in a specific business. Influencers' goal is to convince the audiences of their own ideas, products, or services. There are six principles of persuasion, which are consensus, consistency, scarcity, reciprocity, authority, and liking. Thus, among the objectives of this study is to set the effect of influencers' persuasion on teens' behavior.

To sum up, our hypothesis is as the following:

H1: Social media influencers' distinctive features affect teenagers’ behavior.

Social media influencers' and teenagers’ behavior

Young people and adolescents are increasingly using social media, consequently, they receive a lot of information from different sources that may influence in one way or another their behavior and decisions. Accordingly, the Digital report (2021) (published in partnership with Hootsuite and we Are Social) indicated that connected technologies became an integral part of people's lives, and it has seen great development in the last twelve months especially with regard to social media, e-commerce, video games, and streaming content. According to the statistics raised in the global State of Digital (2021), the number of social media users has increased by 490 million users around the world compared to last year to attain 4.20 billion. In Tunisia, until January 2021 the number of social media users has increased to attain 8.20 million, which represents 69 percent of the total population while 97% accessing via mobile phone. According to the ALEXA report ( 2021 ), Google.com, Facebook and YouTube are the networks most used by Tunisian people. In addition, 18, 5% of Facebook users are under 13 years old.The use of social media by young people has recently increased, which led us to ask about the influence of such an alternative on their psychological and mental conditions, their identity formation, and their self-estimation. One of this study aims is also to answer the question: why teens follow Social media influencers?

Identity formation

Identity formation relates to the complex way in which human beings institute a continued unique view of the self (Erikson, 1950 ). Consequently, this concept has largely attached to terms like self-concept, personality development, and value. Identity, in a simplified way, is an aggregation of the “self-concept, who we are” and “self-awareness” (Aronson et al., 2005 ). In line with communication theory, Scott ( 1987 ) indicated that interpersonal connection is a key factor in identity formation. Most importantly, the individual's identity formation is the cornerstone of building a personality. A stream of research indicates that consumers accept influence from others they identify with and refuse influence when they desire to disconnect (Berger & Heath, 2007 ; White & Dahl, 2006 ).

Adolescence is a transitional stage in individuals' lives that represents the interval between childhood and adulthood (e.g. Hogan & Astone, 1986 ; Sawyer et al., 2018 ). From here begins teens' psychological conflicts that call into question-related to themselves and about their role in society (e.g. Hill et al., 2018 ). In fact, teens go through many experiences because of the physical and psychological changes during the self-establishment phase, which influences not only their identity formation but also their own personality. At this stage, radical changes occur in their lives, which may affect the course of their future life. The family (precisely parents' behaviors) represents the first influencer on their kids' view of themselves, but this is not the main side. In the era of globalization and technological development, social media has become an important role in shaping the identity of adolescents (see Gajaria et al., 2011 ). In the adolescent stage, individuals start to use the flood of information received from various sources (especially from social media) to find out a sense of self and personal identity. Davis ( 2013 ) affirmed that students who communicated online with their peers express better visibility of self-concept. In its turn, self-concept visibility has related to friendship quality. According to Arnett and Hughes ( 2014 ), identity formation is the result of "thinking about the type of person you want to be” (p. 340). Due to the intense appearance of social media in the lives of teenagers, identity formation is highly affected by social media influencers' personalities. Kunkel et al. ( 2004 ) affirmed that targeted advertisements in social media affect the identity molding of teens by encouraging them to espouse new habits of appearance and consumption. Identification is easier when there is a previous model to mimic.

This work aims to explore the effect of social media influencers' distinctive features on the healthy identity development of teens.

Mimetic bias

Investigating mimicry in the psychological literature is not a recent subject. Kendon ( 1970 ) and LaFrance ( 1982 ) were the first researchers that introduce the mimicry concept in literature. Nevertheless, exploring mimicry effect on peoples’ behavior presents a new area of research. Many researchers like Chartrand and Dalton ( 2009 ) and Stel & Vonk ( 2010 ) presented mimicry as the interaction of an individual with others through observing and mirroring their behaviors, attitudes, expressions, and postures. Chartrand and Dalton ( 2009 ) indicated that social surroundings are easily contagious and confirmed the high ability of individuals to mimic what they see in their social environment. Individuals resort to mimicry to fulfill their desire to belong to a group and be active members of society. Therefore, Lakin et al. ( 2003 ) affirmed that mimicry could be used to enhance social links with others. Such behavior aims to bring people closer to each other and create intimacy. White and Argo ( 2011 ) classified mimicry as conscious and unconscious. According to the Neuroscience literature, unconscious mimicry occurs due to the activation of individual mirror neurons that lead to mimic others (e.g. Hatfield et al., 1994 ). Thus, mimickers “automatically” imitate others in many situations like facial expressions (e.g., smiling), behavioral expressions (e.g., laughing), and postural expressions (e.g., hand positioning) (Meltzoff & Moore, 1983 ; LaFrance & Broadbent, 1976 ; Simner, 1971 ). On the other hand, a recent stream of research has advocated conscious mimicry (White & Argo, 2011 ; Ruvio et al., 2013 ). Ruvio et al. ( 2013 ) have presented the "Consumer’s Doppelganger Effect" theory. According to the authors, when consumers have the intention to look like their role models, they imitate them.

One of the paradoxical challenges in the adolescence period is the teens' simultaneous need for "mimic" and "differentiation ".Among the most common questions asked between adolescents is "Who we are?”. The identification of themselves based commonly on a comparison between them and members of the group to which they aim to belong. The feeling of being normal is an obsession that haunts the majority of teenagers. Their sense of being within the norm and not being alienated or disagreed with others prompts teenagers to do anything even if this poses a danger to them just to be accepted by others. Today, with the development of social media, family, peers and friends are no longer the only influencers that teens mimic, but this environment has expanded to include social media influencers. Teens give more attention to their online image and mimic social media influencers to achieve a sense of belonging. According to Cabourg and Manenti ( 2017 ), the content shared by adolescents with each other about their lives on their own social networks helps them understand and discover each other, and create their identity away from their parents. This phenomenon turns into a problem when adolescents mimic each other only not to be excluded or rejected, even if these actions do not represent them.

Another important aim of this study is to explore the effect of social media influencers' distinctive features on teen’s mimicry behavior.

Confirmation bias

Cabourg and Manenti ( 2017 ) pointed out that it is a necessity for a teenager to be a part of a peer group. Belonging to the group for a teenager reinforces his/her sense of existence away from family restrictions. As we have mentioned before and in line with Hernandez et al. ( 2014 ), teens need to create peer relationships, whether to contribute positively or negatively to their psychosocial side and undoubtedly play a crucial role in the development of identity. Araman and Brambilla ( 2016 ) argued that: "Teenage is an important stage in life, full of physical and psychological transformation, awakening in love and professional concerns. Identifying yourself with a group makes you feel stronger, to say that you exist, and even to distinguish yourself from society”. The development of social media platforms promotes the desire of teens to a group belonging. Social media platforms, such as tick-tock, Facebook, and Instagram, motivate their users to interact with likes and comments on others people’s posts. In fact, according to Davis ( 2012 ), casual communication between teens through social networking using text and instant messages enhances their sense of belonging. Furthermore, the author indicates that social media helps teens to compare their ideas and experiences with their peers, which support their sense of belonging. According to Zeng et al. ( 2017 ), social media interactions aim to create strong social bonds and raise emotional belonging to a community. Confirmation bias occurs when an individual cannot think and create outside the herd. Equally important, due to the confirmation bias, teens cannot identify themselves, except by flying inside the swarm. Teens may identify themselves as fans of a famous influencer just to feel the sense of belonging. This work tests the effect of social media influencers' distinctive features on teens’ sense of belonging.

Self-esteem

Psychological literature defines Self-esteem as the individual’s evaluation of himself or herself that can be positive or negative (Smith et al., 2014 ). Coopersmith ( 1965 ) affirmed that the self-esteem is the extent to which an individual views his self as competent and worthwhile. A stream of past works highlighted the effects of social media on self-esteem (Błachnio et al., 2016 ; Denti et al., 2012 ; Gonzales & Hancock, 2011 ). The majority of them found that audiences with low self-esteem use more social networks’ to reinforce their self-esteem. Due to technological developments, social media networks offer a self-comparison between users. According to Festinger ( 1954 ), social media users focus more on self-evaluations by making social comparisons with others concerning many issues like beauty, popularity, social classes or roles, wealth accumulation, etc. Social comparison is a part of building a teen's personal identity (Weinstein, 2017 ). Among adolescents, there are two types of comparisons on social media, which are upward comparison, and downward comparison (Steers et al., 2014 ). The first one has related to weakened levels of self-esteem and high depressive symptoms. The second one is characterized by expanding levels of self-esteem and low levels of anxiety (Burrow & Rainone, 2017 ). According to Wright et al. ( 2018 ), self-presentation on social media is related to the extent to which others accept and the determined level of belonging that based on the number of likes and comments.

This study aims to test the effect of social media influencers' distinctive features on teens’ self-esteem.

Digital distraction

Social media has taken over most of the spare time. It has displaced the time spent on other activities like reading, watching TV, make sports etc.… (Twenge et al., 2019 ). Consequently, the phenomenon of digital distraction has widely spread, especially with the rise of smartphones use. The results of a study established by Luna ( 2018 ) indicated that the use of smartphones during a meal leads to minimize the levels of connectedness and enjoyment and increase the levels of distraction comparing to those who set devices off. Martiz ( 2015 ) found that students with Internet addiction often feel lonely and depressed. Recently, Emerick et al. ( 2019 ) affirmed that the students themselves agree that spending a lot of time using social media leads to distraction. Many studies have proven that most teens spend a lot of time online (e.g., Anderson & Jiang, 2018 ; Twenge et al., 2018 ). Thus, they are the most vulnerable to digital distraction. We believe that whenever distinctive features of influencers are good, the most important impact they have on young people, leads to distraction.

At this level, our second hypothesis is as the following:

H2. The behavior and cognitive biases of teens are affected by social media influence.

Research methods

The cognitive maps.

The cognitive map is relatively an old technique (Huff, 1990 ). However, the use of cognitive maps in scientific research has increased in recent years. According to Axelrod ( 1976 ), a cognitive map is a mathematical model that reflects a belief system of a person. In another words, a cognitive map is a representation of causal assertion way of a person on a limited area. At the beginning of the 1970s, it was intellectually popular amongst behavioral geographers to investigate the significance of cognitive maps, and their impacts on people’s spatial behavior. A cognitive map is a type of mental representation, which serves an individual to acquire, store, recall, code, and decode information about the relative locations and attributes of phenomena in their everyday or metaphorical spatial environment. It is usually defined as the graphical representation of a person belief about a particular field. A map is not a scientific model based on objective reality, but a graphical representation of an individual's specific beliefs and ideas about complex local situations and issues. It is relatively easy for humans to look at maps (cognitive maps in our case) and understand connections, between different concepts. Cognitive maps can therefore also be thought of as graphs. Graphs can be used to represent many interesting things about our world. It can also be used to solve various problems. According to Bueno & Salmeron ( 2009 ), Cognitive Maps are a powerful technique that helps to study human cognitive phenomena and specific topics in the world. This study uses cognitive maps as a tool to investigate the mental schema of teenagers in Tunisian Scouts. In fact, cognitive mapping helps to explore the impact of social media on teenage behavior in the Tunisian context. In other words, we focus on the effect of influencers' distinctive features on teen behavior.

Data collection and sample selection

The aim of this work is to explore the effect of social media influencers' distinctive features on teenagers' behavior in Tunisian context. On the other hand, this work investigates if the psychological health of teens is affected by social media influence. To analyze mentally processing multifactor-interdependencies by the human mind or a scenario with highly complex problems, we need more complex analysis methods like the cognitive map technique.

The questionnaire is one of the appropriate methods used to construct a collective cognitive map (Özesmi & Özesmi, 2004 ). Following Eden and Ackermann ( 1998 ), this study uses face-to-face interviews because it is the most flexible method for data collection and it is the appropriate way to minimize the questionnaire mistiness. The questionnaire contains two parts: the first part is reserved to identify the interviewees. The second part provides the list of concepts for each approach via cross-matrix. The questionnaire takes the form of an adjacency matrix (see Table 1 ). The data collection technique appropriate to build a cognitive map is the adjacent matrix. The adjacency matrix of a graph is an (n × n) matrix:

The variables used in the matrix can be pre-defined (by the interviewer using the previous literature) or it can be identified in the interview by the interviewees. This paper uses the first method to restrict the large number of variables related to both influencers’ distinctive features and teenagers' behavioral biases (see Table 2 ). This work identified two types of social media influencers that are Facebook bloggers and Instagrammers for two reasons. Facebook is the most coveted social network for Tunisians. It has more than 6.9 million active users in 2020 or 75% of the population (+ 13 years) of which 44.9% were female users and 55.1% male. On the other hand, Instagram is the second popular social media platform. It has more than 1.9 million, namely 21% of the Tunisian population (+ 13 years).

In this work, we deal with (10 × 10) adjacency matrix.

Experts (psychologists, academics, etc.) often analyze the relationships between social media and young people’s behavior. The contribution of this work is that we rely on the adolescents' point of view in order to test this problem using the cognitive maps method. To our knowledge, no similar research has been done before.

This work is in parallel to the framework of the Tunisian State project "Strengthening the partnership between the university and the economic and social environment". It aims to merge the scientific track with the association work. We have organized an intellectual symposium in conjunction with the Citizen Journalism Club of youth home and the Mohamed-Jlaiel Scouts Group of Mahres entitled "Social Influencers and Their Role in Changing Youth Behaviors”.This conference took place on April 3, 2021, in the hall of the municipality, under the supervision of an inspector of youth and childhood”. In fact, Scouts is a voluntary educational movement that aims to contribute to the development of young people to reach the full benefit of their physical and social capabilities to make them responsible individuals. Scouts offer children and adolescents an educational space complementary to that of the family and the school. The association emphasizes community life, taking responsibility, and learning resourcefulness.Scouting contributes to enhancing the individual's self-confidence and sense of belonging and keeps them away from digital distraction. Therefore, our sample has based on a questionnaire answered by young people belonging to the Tunisian Scoutsaged between 14 and 17 and, who belong to the Mohamed-Jlaiel Scouts Group of Mahres. In fact, scouting strengthens the willpower of young people and allows them to expand their possibilities for self-discipline. In addition, Scout youth are integrated into the community and spend more time in physical and mental activities than their peers who spend most of their free time on social media. Unfortunately, because of the epidemiological situation that Tunisia experienced during this period due to the spread of the Coronavirus, we could not summon more than 35 people, and the first sample was limited only to 25 young people. Thus, a second study with another data collection is needed. Over two successive months (November and December 2021), we make a few small workshops (due to the pandemic situation) with scouts’ young people. The second sample contains 38 teens. Therefore, our total data hold 63young people (26 female and 37 male). It should be noted that the surveys were carried out after parental consent.

We start our interviews with presenting the pros and cons of social mediaand its effect on audiences’ behavior. After forming an idea with the topic, we asked young people to answer the questionnaire presented to them after we defined and explained all the variables. We have directly supervised the questionnaire. Teens are invited to fulfill the questionnaire (in the form of a matrix) using four possibilities:

If variable i has no influence on variable j, the index (i, j) takes a value of zero

1 if variable I has a weak influence on variable j.

2 if variable I has a strong influence on variable j.

3 if variable I has a very strong influence on variable j.

To sumup, the final data contains 63 individual matrices. The aim of the questionnaire is then to build the perception maps (Lajnef et al., 2017 ).

Collective cognitive map method

This work is of qualitative investigation. The research instrument used in this study is the cognitive approach. This work aims to create a collective cognitive map using an interviewing process. Young peopleare invited to fill the adjacencymatrices by giving their opinion about the effect of social media influencers' distinctive features on teenagers' behavior. To draw up an overall view, individual maps (creating based on adjacency matrices) aggregated to create a collective cognitive map. Since individual maps denote individual thinking, collective map is used to understand the group thinking. The aggregation map aimed to show the point of similarities and differences between individuals (Lajnef et al., 2017 ). The cognitive map has formed essentially by two elements: concepts (variables) and links (relations between variables). The importance of a concept is mainly related to its link with other variables.

This technique helps to better understand the individual and collective cognitive universe. A cognitive map became a mathematical model that reflects a belief system of individuals since the pioneering work of Tolman ( 1948 ). Axelrod ( 1976 ) investigated the political and economic field and considered "cognitive maps" as graphs, reflecting a mental model to predict, understand and improve people's decisions. Recently, Garoui & Jarboui ( 2012 ) have defined the cognitive map as a tool aimed to view certain ideas and beliefs of an individual in a complex area. This work aims to explore a collective cognitive map to set the complex relationships between teenagers and social media influencers. For this reason, we investigate the effect of social media influencers' distinctive features on teenagers' behavior using an aggregated cognitive map.

Results and discussion

In this study, we report all measures, manipulations and exclusions.

Structural analysis and collective cognitive map

This paper uses the structural analysis method to test the relationship between the concepts and to construct a collective cognitive map. According to Godet et al. ( 2008 ), the structural analysis is “A systematic, matrix form, analysis of relations between the constituent variables of the studied system and those of its explanatory environment”. The structural analysis purpose is aimed to distinguish the key factors that identify the evolution of the system based on a matrix that determines the relationships among them (Villacorta et al., 2012 ). To deal with our problem, Micmac software allows us to treat the collected information in the form of plans and graphs in order to configure the mental representation of interviewees.

The influence × dependence chart

This work uses the factor analysis of the influence-dependence chart in which factors have categorized due to their clustered position. The influence × dependence plan depends on four categories of factors, which are the determinants variables, the result variables the relay variables, and the excluded variables. The chart has formed by four zones presented as the following (Fig.  1 ):

Influence-dependence chart, according to MICMAC method

Zone 1: Influent or determinant variables

Influent variables are located in the top left of the chart. According to Arcade et al. ( 1999 ) this category of variables represents a high influence and low dependence. These kinds of variables play and affect the dynamics of the whole system, depending on how much we can control them as key factors. The obtained results identify uniqueness, trustworthiness, and Mimetic as determinant variables. The ability of influencers’ is to provide personalized and unique content that influence Tunisian teens’ behavior. This finding is in line with Casaló et al. ( 2020 ) work. On the other hand, the results indicate that teens mimic social media influencers to feel their belonging. Such an act allows them to discover each other, and create their identity away from their parents (Cabourg & Manenti, 2017 ). The most Influential variable of the system is trustworthiness.The more trustworthiness influencers via social media are, the higher their influence on young people will be. This finding is conformed to previous studies (Giffin, 1967 ; Spry et al., 2011 ).

Zone 2: Relay variables

The intermediate or relay variables are situated at the top right of the chart. These concepts have characterized by high influence and sensitivity. They are also named “stake factors” because they are unstable. Relay variables influence the system depending on the other variables. Any effect of these factors will influence themselves and other external factors to adjust the system. In this study, most of influencers' distinctive features (persuasion, originality, and expertise) play the role of relay variables. The results indicate that the influence of persuasion affects young people's convictions, depending on other variables. The results are in line with previous studies (e.g. Perloff, 2008 ; Shen et al., 2013 ). Furthermore, the findings indicate that the more expertise social media influencers' are, the higher their influence on young people will be. The study of Ki and Kim ( 2019 ) supported our findings. Additionally, the originality of the content presented on social media attracts the audience more than the standard content. The results are in line with those of Khamis et al., ( 2017 ) and Djafarova & Rushworth ( 2017 ).

Based on the results of zone 1 and zone 2, we can sum up that Social media influencers' distinctive features tested on this work affect teenagers’ behavior. Therefore, H1 is accepted.

Zone 3: Excluded or autonomous variables

The excluded variables are positioned in the bottom left of the chart. This category of variables is characterized by a low level of influence and dependence. Such variables have no impact on the overall dynamic changes of the system because their distribution is very close to the origin. This work did not obtain this class of variables.

Zone 4: Dependent variables

The dependent variables are located at the bottom right of the chart. These variables have characterized by a low degree of influence and a high degree of dependence. These variables are less influential and highly sensitive to the rest of variables (influential and relay variables). According to our results, the dependent variables are those related to teens' behavior and cognitive biases. Social media influencers affect the identity development of teens. These findings are in line with those of Kunkel et al. ( 2004 ).The results show also that young people often identify themselves as fans of a famous influencer just to feel the belonging. These results are in line with previous studies like those of Davis ( 2012 ) and Zeng et al. ( 2017 ). Furthermore, the findings indicate that young people use more social networks’ to reinforce their self-esteem.The results confirm with those of Denti et al. ( 2012 ) and Błachnio et al. ( 2016 ).Influencers via social media play a role in digital distraction. Thus, the result found by Emerick et al. ( 2019 ) supports our findings.

Based on the results of zone 3, we can sum up that the behavior and cognitive biases of teens are affected by social media influencers. Therefore, H2 is accepted.

Collective cognitive maps

During this study, we have gathered the individuals’ matrices to create a collective cognitive mind map. The direct influence graph (Figs.  2 and 3 ) present many interesting findings. First, the high experience of influencers via social media enhances the production of original content. Furthermore, the more expertise the influencers' are, the higher their degree of persuasion on young people will be. As similar to this work, Kirmani et al. ( 2004 ) found that the influencers' experience with persuasion emerges as factors that affect customers. Beside the experience, the more an influencer provides unique and uncirculated content specific to him, the higher the originality of the content will be. Previous studies hypothesized that unique ideas are the most stringent method for producing original ideas (e.g., Wallach & Kogan,  1965 ; Wallach & Wing, 1969 ).Generally; influencers that produce different contents have a great popularity because they produce new trends. Therefore, our results indicate that young people want to be one of their fans just to feel their belonging. Furthermore, our findings indicate that the originality of content can be a source of digital distraction. Teenagers spend a lot of time on social media to keep up with new trends (e.g. Chassiakos & Stager, 2020 ).

The collective cognitive maps (25% of links)

The collective cognitive map (100% of links)

The influencers' experience and their degree of trustworthiness, besides the originality of the content, enhance their abilities to persuade adolescents. During adolescence, young people look for a model to follow. According to our results, it can be a social media influencer with a great ability to persuade.

In recent years, the increasing use of social media has enabled users to obtain a large amount of information from different sources. This evolution has affected in one way or another audience's behavior, attitudes, and decisions, especially the young people. Therefore, this study contributes to the literature in many ways. On the first hand, this paper presents the most distinctive features of social media influencers' and tests their effect on teenagers' behavior using a non-clinical sample of young Tunisians. On the other hand, this paper identifies teens' motivations for following social media influencers. This study exercises a new methodology. In fact, it uses the cognitive approach based on structural analysis. According to Benjumea-Arias et al. ( 2016 ), the aim of structural analysis is to determine the key factors of a system by identifying their dependency or influence, thus playing a role in decreasing system complexity. The present study successfully provides a collective cognitive map for a sample of Tunisian young people. This map helps to understand the impact of Facebook bloggers and Instagrammers on Tunisian teen behavior.

This study presents many important findings. First, the results find that influencers' distinctive features tested on this work affect teenagers’ behavior. In fact, influencers with a high level of honesty and sincerity prove trustworthiness among teens. This result is in line with those of Giffin ( 1967 ). Furthermore, the influencer’s ability to provide original and unique content affects the behavior of teens. These findings confirm those of Casaló et al. ( 2020 ). In addition, the ability to influence is related with the ability to persuade and expertise.

The findings related to the direct influence graph reveal that the influencers' distinctive features are interconnected. The experience, the degree of trustworthiness, and the originality of the submitted content influence the ability of an influencer to persuade among adolescents. In return, the high degree of persuasion impresses the behavior, attitudes, and decisions of teens with influences in their identity formation. The high experience and uniqueness help the influencer to make content that is more original. Young people spend more time watching original content (e.g. Chassiakos & Stager, 2020 ). Thus, the originality of content can be a source of digital distraction.

The rise in psychological problems among adolescents in Tunisia carries troubling risks. According to MICS6 Survey (2020), 18.7% of children aged 15–17 years suffer from anxiety, and 5.2% are depressed. The incidence of suicide among children (0–19 years old) was 2.07 cases per 100,000 in 2016, against 1.4 per 100,000 in 2015. Most child suicides concern 15–19-year-olds. They are in part linked to intensive use of online games, according to the general delegate of child protection. However, scientific studies rarely test the link between social media use and psychological disorders for young people in the Tunisian context. In fact, our result emphasized the important role of influencers' distinctive features and their effect on teens' behavior.

Thus, it is necessary and critical to go deeper into those factors that influence the psychological health of teens. We promote researchers to explore further this topic. They can uncover ways to help teens avoid various psychological and cognitive problems, or at least realize them and know the danger they can cause to themselves and others.

These results have many implications for different actors like researchers and experts who were interested in the psychological field.

This work suffers from some methodological and contextual limitations that call recommendations for future research. Fist, the sample size used is relatively small because of the epidemiological situation that Tunisia experienced at the time of completing this work. On the other hand, this work was limited only to study the direct relationship between variables. Therefore, we suggest expanding the questionnaire circle. We can develop this research by interviewing specialists in the psychological field. From an empirical point of view, we can go deeper into this topic by testing the indirect relationship among variables.

Alexa. (2021). Amazon Alexa. Retrieved January 24, 2021 from https://www.alexa.com/topsites/countries/TN

Alotaibi, T. S., Alkhathlan, A. A., & Alzeer, S. S. (2019). Instagram shopping in Saudi Arabia: What influences consumer trust and purchase decisions. International Journal of Advanced Computer Science and Applications , 10 (11). https://thesai.org/Publications/ViewPaper?Volume=10&Issue=11&Code=IJACSA&SerialNo=81

Anderson, M. (2018, May 31). Teens, social media and technology 2018.  Pew Research Center: Internet, Science & Tech . Retrieved January 1, 2020 from https://www.pewresearch.org/internet/2018/05/31/teens-social-mediatechnology-2018/

Anderson, M., & Jiang, J. (2018). Teens, social media & technology 2018. Pew Research Center, 31 (2018), 1673–1689.

Google Scholar  

Araman T., & Brambilla, P. (2016). School in the digital age. Migros magazine - MM46 . pp. 13–19.

Arcade. J, Godet .M, Meunier. F, Roubelat. F. (1999). Structural analysis with the MICMAC method & Actor's strategy with MACTOR method. In J. Glenn (Ed.), Futures research methodology . American Council for the United Nations University, the millennium project.

Arnett, J. J. (2000). Emerging adulthood a theory of development from the late teens through the twenties. American Psychologist, 55 , 469–480.

Article   PubMed   Google Scholar  

Arnett, J. J., & Hughes, M. (2014). Adolescence and emerging adulthood (pp. 102–111). Pearson.

Book   Google Scholar  

Aronson, E., Wilson, T. D., & Akert, R. M. (2005). Social psychology (Vol. 5). Prentice Hall.

Audrezet, A., de Kerviler, G., & Moulard, J. G. (2020). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of business research, 117 , 557–569.‏

Axelrod, R. (1976). The cognitive mapping approach to decision making. Structure of Decision, 1 (1), 221–250.

Bandura, A., & Adams, N. E. (1977). Analysis of self-efficacy theory of behavioral change. Cognitive Therapy and Research, 1 (4), 287–310.

Article   Google Scholar  

Benjumea-Arias, M., Castañeda, L., & Valencia-Arias, A. (2016). Structural analysis of strategic variables through micmac use: Case study. Mediterranean Journal of Social Sciences, 7 (4), 11.

Berger, J., & Heath, C. (2007). Where consumers diverge from others: Identity signaling and product domains. Journal of Consumer Research, 34 , 121–134.

Błachnio, A., Przepiorka, A., & Pantic, I. (2016). Association between Facebook addiction, self-esteem and life satisfaction: A cross-sectional study. Computers in Human Behavior, 55 , 701–705.

Boerman, S. C. (2020). The effects of the standardized Instagram disclosure for micro-and meso-influencers. Computers in Human Behavior, 103 , 199–207.

Bueno, S., & Salmeron, J. L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 36 (3), 5221–5229.

Burrow, A. L., & Rainone, N. (2017). How many likes did I get?: Purpose moderates links between positive social media feedback and self-esteem. Journal of Experimental Social Psychology, 69 , 232–236.

Cabourg, C., & Manenti, B. (2017). Portables: La face cachée des ados . Flammarion.

Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2020). Influencers on instagram: Antecedents and consequences of opinion leadership. Journal of business research, 117 , 510–519.‏‏

Chahal, M. (2016). Four trends that will shape media in 2016. Marketing Week. Available from: http://www.marketingweek.Com/2016/01/08/four-trendsthat-will-shape-media-in-2016 . Accessed 1 May 2018.

Chartrand T. L., Dalton A. N. (2009). Mimicry: Its ubiquity, importance, and functionality. In Morsella E., Bargh J. A., Gollwitzer P. M. (Eds.), Oxford handbook of human action (pp. 458–483). New York, NY: Oxford University Press.

Chassiakos, Y. R., & Stager, M. (2020). Chapter 2 - Current trends in digital media: How and why teens use technology. In M. A. Moreno & A. J. Hoopes (Eds.), Technology and adolescent health (pp. 25–56). Academic Press. https://doi.org/10.1016/B978-0-12-817319-0.00002-5

Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13 (4), 9–38.

Colliander, J. (2019). “This is fake news”: Investigating the role of conformity to other users’ views when commenting on and spreading disinformation in social media. Computers in Human Behavior, 97 , 202–215.

Coopersmith, S. (1965). The antecedents of self-esteem . Princeton.

Crano, W. D., & Prislin, R. (2011). Attitudes and attitude change . Psychology Press.

Dacey, J. S., & ve Kenny, M. (1994). Adolescent development . Brown ve Benchmark Publishers.

Davis, K. (2012). Friendship 2.0: Adolescents’ experiences of belonging and self-disclosure online. Journal of Adolescence, 35 (6), 1527–1536.

Davis, T. (2013). Building and using a personal/professional learning network with social media. The Journal of Research in Business Education, 55 (1), 1.

De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36 (5), 798–828.

Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11 (4), 227–268.

Denti, L., Barbopuolos, I., Nilsson, I., Holmberg, L., Thulin, M., Wendeblad, M., Andén, L., & Davidsson, E. (2012). Sweden’s largest facebook study. Gothenburg Research Institute, 2012 :3.

Derbaix, C., & Vanhamme, J. (2003). Inducing word-of-mouth by eliciting surprise–a pilot investigation. Journal of Economic Psychology, 24 (1), 99–116.

Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51 (3), 629.

Djafarova, E., & Rushworth, C. (2017). Exploring the credibility of online celebrities’ Instagram profiles in influencing the purchase decisions of young female users. Computers in Human Behavior, 68 , 1–7.

Eden, C., & Ackermann, F., (1998). Analyzing and comparing idiographic causal maps. In Eden, C., Spender, J.-C. (Eds.), Managerial and organizational cognition theory, methods and research . Sage, London, pp. 192–209

Elkind, D. (1967). Egocentrism in adolescence. Child Development, 38 (4), 1025–1033.

Emerick, E., Caldarella, P., & Black, S. J. (2019). Benefits and distractions of social media as tools for undergraduate student learning. College Student Journal, 53 (3), 265–276.

Erikson, E. H. (1950). Childhood and society . Norton.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7 (2), 117–140.

Gajaria, A., Yeung, E., Goodale, T., & Charach, A. (2011). Beliefs about attention- deficit/hyperactivity disorder and response to stereotypes: Youth postings in facebook groups. Journal of Adolescent Health, 49 (1), 15–20.

Garoui, N., Jarboui, A., (2012). Cognitive approach of corporate governance: A visualization test of mental models with cognitive mapping technique.  Romanian Economic Journal, 15 (43), 61–96.

Gentina, E., Butori, R., & Heath, T. B. (2014). Unique but integrated: The role of individuation and assimilation processes in teen opinion leadership. Journal of Business Research, 67 (2), 83–91.

Giffin, K. (1967). Interpersonal trust in small-group communication. Quarterly Journal of Speech, 53 (3), 224–234.

Godet, M., Durance, P. H., & Gerber. (2008). Strategic foresight (la prospective): use and misuse of scenario building . LIPSOR Working Paper (Cahiers du LIPSOR).

Gonzales, A. L., & Hancock, J. T. (2011). Mirror, mirror on my Facebook wall: Effects of exposure to Facebook on self-esteem. Cyberpsychology, Behavior, and Social Networking, 14 (1–2), 79–83.

Hashoff. (2017) Influencer marketer . A #Hashoff state of the union report. Available at:  https://www.hashoff.com/ . Accessed October 2019.

Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1994). Emotional contagion . Cambridge University Press.

Hernandez, L., Oubrayrie-Roussel, N., & Lender, Y. (2014). Self-affirmation in the group of peers to school demobilization. In NecPlus (Ed.) Childhood (2), pp. 135–157. Recovered on https://www.cairn.info/revue-enfance2-2014-2-page-135.htm

Hill, R. M., Del Busto, C. T., Buitron, V., & Pettit, J. W. (2018). Depressive symptoms and perceived burdensomeness mediate the association between anxiety and suicidal ideation in adolescents. Archives of Suicide Research, 22 (4), 555–568.

Hogan, D., & Astone, N. (1986). The transition to adulthood. Annual Review of Sociology, 12 , 109–130.

Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion: Psychological studies of opinion change . New Haven, CT: Yale University Press.

Huff, A. S. (1990). Mapping strategic thought. In A. S. Huff (Ed.), Mapping strategic thought (pp. 11–49). Wiley.

Jahoda, G. (1959). Development of the perception of social differences in children from 6 to 10. British Journal of Psychology, 50 (2), 159–175.

Kendon, A. (1970). Movement coordination in social interaction: Some examples described. Actapsychologica, 32 , 101–125.

Khamis, S., Ang, L., & Welling, R. (2017). Self-branding, ‘micro-celebrity’and the rise of Social Media Influencers. Celebrity Studies, 8 (2), 191–208.

Ki, C. W. C., & Kim, Y. K. (2019). The mechanism by which social media influencers persuade consumers: The role of consumers’ desire to mimic. Psychology & Marketing, 36 (10), 905–922.

Kirmani, A., & Campbell, M. C. (2004). Goal seeker and persuasion sentry: How consumer targets respond to interpersonal marketing persuasion. Journal of Consumer Research, 31 (3), 573–582.

Kunkel, D., Wilcox, B. L., Cantor, J., Palmer, E., Linn, S., & Dowrick, P. (2004). Report of the APA task force on advertising and children. Washington, DC: American Psychological Association, 30 , 60.

LaFrance, M., & Broadbent, M. (1976). Group rapport: Posture sharing as a nonverbal indicator. Group & Organization Studies, 1 (3), 328–333.

LaFrance, M. (1982). Posture mirroring and rapport: Interaction rhythms. New York: Human Sciences Press, 279–298.

Lajnef, K., Ellouze, S., & Mohamed, E. B. (2017). How to explain accounting manipulations using the cognitive mapping technique? An evidence from Tunisia. American Journal of Finance and Accounting, 5 (1), 31–50.

Lakin, J. L., Jefferis, V. E., Cheng, C. M., & Chartrand, T. L. (2003). The chameleon effect as social glue: Evidence for the evolutionary significance of nonconscious mimicry. Journal of Nonverbal Behavior, 27 (3), 145–162.

Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1944). The people’s choice: How the voter makes up his mind in a presidential campaign . Duell, Sloan and Pearce.

Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19 (1), 58–73.

Loureiro, S. M. C., & Sarmento, E. M. (2019). Exploring the determinants of instagram as a social network for online consumer-brand relationship. Journal of Promotion Management, 25 (3), 354–366.

Luna, K. (2018). Dealing with digital distraction. Retrieved January 2, 2020 from https://www.apa.org/news/press/releases/2018

Martiz, G. (2015). A qualitative case study on cell phone appropriation for language learning purposes in a Dominican context . Utah State University.

Marwick, A. E. (2015). Status update: Celebrity, publicity, and branding in the social media age . Wiley.

Marwick, A. (2013). They’re really profound women, they’re entrepreneurs. Conceptions of authenticity in fashion blogging. In 7th international AIII conference on weblogs and social media (ICWSM), July (vol. 8).

Maslach, C., Stapp, J., & Santee, R. T. (1985). Individuation: Conceptual Analysis and Assessment. Journal of Personality and Social Psychology, 49 (September), 729–738.

McCroskey, J. C. (1966). Scales for the measurement of ethos. Speech Monographs, 33 (1), 65–72.

Meltzoff, A. N., & Moore, M. K. (1983). Newborn infants imitate adult facial gestures. Child Development  54 (3), 702–709.

Moore, A., Yang, K., & Kim, H. M. (2018). Influencer marketing: Influentials’ authenticity, likeability and authority in social media. In International Textile and Apparel Association Annual Conference Proceedings . Iowa State University Digital Press.

Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19 (3), 39–52.

Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176 (1–2), 43–64.

Perloff, R. M. (2008). Political Communication: Politics, Press, and Public in America. Boca Raton, FL: Routledge. The SAGE Handbook of Persuasion, 258–277.

Pligt, J., & Vliek, M. (2016). The Psychology of Influence: Theory, research and practice . Routledge.

Ruvio, A., Gavish, Y., & Shoham, A. (2013). Consumer’s doppelganger: A role model perspective on intentional consumer mimicry. Journal of Consumer Behaviour, 12 (1), 60–69.

Sawyer, S. M., Azzopardi, P. S., Wickremarathne, D., & Patton, G. C. (2018). The age of adolescence. The Lancet Child & Adolescent Health, 2 (3), 223–228.

Scheer, L. K., & Stern, L. W. (1992). The effect of influence type and performance outcomes on attitude toward the influencer. Journal of Marketing Research (JMR), 29 (1), 128–142.

Scott, W. R. (1987). The adolescence of institutional theory. Administrative Science Quarterly, 32 (4), 493–511.

Shen, L. J., & Bigsby, E. (2013). The effects of message features: Content, structure, and style. In J. P. Dillard & L. Shen (Eds.), The Sage handbook of persuasion: Developments in theory and practice (2nd ed., pp. 20–35). Los Angeles, CA: Sage.

Simner, M. L. (1971). Newborn’s response to the cry of another infant. Developmental Psychology, 5 (1), 136.

Sireni. (2020). The role of Instagram influencers and their impact on millennials’ consumer behaviour. Theseus. http://www.theseus.fi/handle/10024/347659

Smith, E. R., Mackie, D. M., & Claypool, H. M. (2014). Social psychology. https://doi.org/10.4324/9780203833698 .

Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53 , 1–9. https://doi.org/10.1016/j.jretconser.2019.01.011

Spry, A., Pappu, R., & Bettina Cornwell, T. (2011). Celebrity endorsement, brand credibility and brand equity. European Journal of Marketing, 45 (6), 882–909.

Steers, M. L. N., Wickham, R. E., & Acitelli, L. K. (2014). Seeing everyone else’s highlight reels: How Facebook usage is linked to depressive symptoms. Journal of Social and ClinicalPsychology, 33 (8), 701–731.

Stel, M., & Vonk, R. (2010). Mimicry in social interaction: Benefits for mimickers, mimickees, and their interaction. British Journal of Psychology, 101 (2), 311–323.

Tajfel, H. (1972). La catégorisation sociale. In S. Moscovici (Ed.), Introduction à la psychologie sociale (pp. 272–302). Larousse.

Tarsakoo, P., & Charoensukmongkol, P. (2019). Dimensions of social media marketing capabilities and their contribution to business performance of firms in Thailand. Journal of Asia Business Studies, 14 (4), 441–461. https://doi.org/10.1108/jabs-07-2018-0204

Teng, S., Khong, K. W., Goh, W. W., & Chong, A. Y. L. (2014). Examining the antecedents of persuasive eWOM messages in social media. Online Information Review, 38 (6), 746–768.

Tian, K. T., Bearden, W. O., & Hunter, G. L. (2001). Consumers’ need for uniqueness: Scale development and validation. Journal of Consumer Research, 28 (1), 50–66.

Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55 (4), 189.

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6 (1), 3–17.

Twenge, J. M., Martin, G. N., & Spitzberg, B. H. (2019). Trends in US Adolescents’ media use, 1976–2016: The rise of digital media, the decline of TV, and the (near) demise of print. Psychology of Popular Media Culture, 8 (4), 329.

Villacorta, P. J., Masegosa, A. D., Castellanos, D., & Lamata, M. T. (2012). A linguistic approach to structural analysis in prospective studies. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 150–159). Springer.

Vollenbroek, W., De Vries, S., Constantinides, E., & Kommers, P. (2014). Identification of influence in social media communities. International Journal of Web Based Communities, 10 (3), 280–297.

Wallach, M. A., & Kogan, N. (1965). Modes of thinking in young children . New York, NY: Holt, Rinehart, & Winston.

Wallach, M. A., & Wing, C. W. (1969). The talented student: A validation of the creativity- intelligence distinction. New York, NY: Holt, Rinehart & Winston

Weinstein, E. (2017). Adolescents' differential responses to social media browsing: Exploring causes and consequences for intervention. Computers in Human Behavior, 76 , 396–405.‏‏

White, K., & Argo, J. J. (2011). When imitation doesn’t flatter: The role of consumer distinctiveness in responses to mimicry. Journal of Consumer Research, 38 (4), 667–680.

White, K., & Dahl, D. W. (2006). To be or not be? The influence of dissociative reference groups on consumer preferences. Journal of Consumer Psychology, 16 (4), 404–414.

Wright, E. J., White, K. M., & Obst, P. L. (2018). Facebook false self-presentation behaviors and negative mental health. Cyberpsychology, Behavior, and Social Networking, 21 (1), 40–49.

Zeng, F., Tao, R., Yang, Y., & Xie, T. (2017). How social communications influence advertising perception and response in online communities? Frontiers in Psychology, 8 , 1349.

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Lajnef, K. The effect of social media influencers' on teenagers Behavior: an empirical study using cognitive map technique. Curr Psychol 42 , 19364–19377 (2023). https://doi.org/10.1007/s12144-023-04273-1

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SOCIAL MEDIA USE BY TWEENS AND TEENS

Benefits of children and adolescents using social media, socialization and communication, enhanced learning opportunities, accessing health information, risks of youth using social media, cyberbullying and online harassment, facebook depression, privacy concerns and the digital footprint, influence of advertisements on buying, on too young: mixed messages from parents and the law, the role of pediatricians, lead authors, council on communications and media executive committee, 2010–2011, past executive committee members, the impact of social media on children, adolescents, and families.

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Gwenn Schurgin O'Keeffe , Kathleen Clarke-Pearson , Council on Communications and Media; The Impact of Social Media on Children, Adolescents, and Families. Pediatrics April 2011; 127 (4): 800–804. 10.1542/peds.2011-0054

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Using social media Web sites is among the most common activity of today's children and adolescents. Any Web site that allows social interaction is considered a social media site, including social networking sites such as Facebook, MySpace, and Twitter; gaming sites and virtual worlds such as Club Penguin, Second Life, and the Sims; video sites such as YouTube; and blogs. Such sites offer today's youth a portal for entertainment and communication and have grown exponentially in recent years. For this reason, it is important that parents become aware of the nature of social media sites, given that not all of them are healthy environments for children and adolescents. Pediatricians are in a unique position to help families understand these sites and to encourage healthy use and urge parents to monitor for potential problems with cyberbullying, “Facebook depression,” sexting, and exposure to inappropriate content.

Engaging in various forms of social media is a routine activity that research has shown to benefit children and adolescents by enhancing communication, social connection, and even technical skills. 1   Social media sites such as Facebook and MySpace offer multiple daily opportunities for connecting with friends, classmates, and people with shared interests. During the last 5 years, the number of preadolescents and adolescents using such sites has increased dramatically. According to a recent poll, 22% of teenagers log on to their favorite social media site more than 10 times a day, and more than half of adolescents log on to a social media site more than once a day. 2   Seventy-five percent of teenagers now own cell phones, and 25% use them for social media, 54% use them for texting, and 24% use them for instant messaging. 3   Thus, a large part of this generation's social and emotional development is occurring while on the Internet and on cell phones.

Because of their limited capacity for self-regulation and susceptibility to peer pressure, children and adolescents are at some risk as they navigate and experiment with social media. Recent research indicates that there are frequent online expressions of offline behaviors, such as bullying, clique-forming, and sexual experimentation, 4   that have introduced problems such as cyberbullying, 5   privacy issues, and “sexting.” 6   Other problems that merit awareness include Internet addiction and concurrent sleep deprivation. 7  

Many parents today use technology incredibly well and feel comfortable and capable with the programs and online venues that their children and adolescents are using. Nevertheless, some parents may find it difficult to relate to their digitally savvy youngsters online for several reasons. Such parents may lack a basic understanding of these new forms of socialization, which are integral to their children's lives. 8   They frequently do not have the technical abilities or time needed to keep pace with their children in the ever-changing Internet landscape. 8   In addition, these parents often lack a basic understanding that kids' online lives are an extension of their offline lives. The end result is often a knowledge and technical skill gap between parents and youth, which creates a disconnect in how these parents and youth participate in the online world together. 9  

Social media sites allow teens to accomplish online many of the tasks that are important to them offline: staying connected with friends and family, making new friends, sharing pictures, and exchanging ideas. Social media participation also can offer adolescents deeper benefits that extend into their view of self, community, and the world, including 1 , 10   :

opportunities for community engagement through raising money for charity and volunteering for local events, including political and philanthropic events;

enhancement of individual and collective creativity through development and sharing of artistic and musical endeavors;

growth of ideas from the creation of blogs, podcasts, videos, and gaming sites;

expansion of one's online connections through shared interests to include others from more diverse backgrounds (such communication is an important step for all adolescents and affords the opportunity for respect, tolerance, and increased discourse about personal and global issues); and

fostering of one's individual identity and unique social skills. 11  

Middle and high school students are using social media to connect with one another on homework and group projects. 11   For example, Facebook and similar social media programs allow students to gather outside of class to collaborate and exchange ideas about assignments. Some schools successfully use blogs as teaching tools, 12   which has the benefit of reinforcing skills in English, written expression, and creativity.

Adolescents are finding that they can access online information about their health concerns easily and anonymously. Excellent health resources are increasingly available to youth on a variety of topics of interest to this population, such as sexually transmitted infections, stress reduction, and signs of depression. Adolescents with chronic illnesses can access Web sites through which they can develop supportive networks of people with similar conditions. 13   The mobile technologies that teens use daily, namely cell phones, instant messaging, and text messaging, have already produced multiple improvements in their health care, such as increased medication adherence, better disease understanding, and fewer missed appointments. 14   Given that the new social media venues all have mobile applications, teenagers will have enhanced opportunities to learn about their health issues and communicate with their doctors. However, because of their young age, adolescents can encounter inaccuracies during these searches and require parental involvement to be sure they are using reliable online resources, interpreting the information correctly, and not becoming overwhelmed by the information they are reading. Encouraging parents to ask about their children's and adolescents' online searches can help facilitate not only discovery of this information but discussion on these topics.

Using social media becomes a risk to adolescents more often than most adults realize. Most risks fall into the following categories: peer-to-peer; inappropriate content; lack of understanding of online privacy issues; and outside influences of third-party advertising groups.

Cyberbullying is deliberately using digital media to communicate false, embarrassing, or hostile information about another person. It is the most common online risk for all teens and is a peer-to-peer risk.

Although “online harassment” is often used interchangeably with the term “cyberbullying,” it is actually a different entity. Current data suggest that online harassment is not as common as offline harassment, 15   and participation in social networking sites does not put most children at risk of online harassment. 16   On the other hand, cyberbullying is quite common, can occur to any young person online, and can cause profound psychosocial outcomes including depression, anxiety, severe isolation, and, tragically, suicide. 17  

Sexting can be defined as “sending, receiving, or forwarding sexually explicit messages, photographs, or images via cell phone, computer, or other digital devices.” 18   Many of these images become distributed rapidly via cell phones or the Internet. This phenomenon does occur among the teen population; a recent survey revealed that 20% of teens have sent or posted nude or seminude photographs or videos of themselves. 19   Some teens who have engaged in sexting have been threatened or charged with felony child pornography charges, although some states have started characterizing such behaviors as juvenile-law misdemeanors. 20 , 21   Additional consequences include school suspension for perpetrators and emotional distress with accompanying mental health conditions for victims. In many circumstances, however, the sexting incident is not shared beyond a small peer group or a couple and is not found to be distressing at all. 4  

Researchers have proposed a new phenomenon called “Facebook depression,” defined as depression that develops when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression. 22 , – , 27   Acceptance by and contact with peers is an important element of adolescent life. The intensity of the online world is thought to be a factor that may trigger depression in some adolescents. As with offline depression, preadolescents and adolescents who suffer from Facebook depression are at risk for social isolation and sometimes turn to risky Internet sites and blogs for “help” that may promote substance abuse, unsafe sexual practices, or aggressive or self-destructive behaviors.

The main risk to preadolescents and adolescents online today are risks from each other, risks of improper use of technology, lack of privacy, sharing too much information, or posting false information about themselves or others. 28   These types of behavior put their privacy at risk.

When Internet users visit various Web sites, they can leave behind evidence of which sites they have visited. This collective, ongoing record of one's Web activity is called the “digital footprint.” One of the biggest threats to young people on social media sites is to their digital footprint and future reputations. Preadolescents and adolescents who lack an awareness of privacy issues often post inappropriate messages, pictures, and videos without understanding that “what goes online stays online.” 8   As a result, future jobs and college acceptance may be put into jeopardy by inexperienced and rash clicks of the mouse. Indiscriminate Internet activity also can make children and teenagers easier for marketers and fraudsters to target.

Many social media sites display multiple advertisements such as banner ads, behavior ads (ads that target people on the basis of their Web-browsing behavior), and demographic-based ads (ads that target people on the basis of a specific factor such as age, gender, education, marital status, etc) that influence not only the buying tendencies of preadolescents and adolescents but also their views of what is normal. It is particularly important for parents to be aware of the behavioral ads, because they are common on social media sites and operate by gathering information on the person using a site and then targeting that person's profile to influence purchasing decisions. Such powerful influences start as soon as children begin to go online and post. 29   Many online venues are now prohibiting ads on sites where children and adolescents are participating. It is important to educate parents, children, and adolescents about this practice so that children can develop into media-literate consumers and understand how advertisements can easily manipulate them.

Many parents are aware that 13 years is the minimum age for most social media sites but do not understand why. There are 2 major reasons. First, 13 years is the age set by Congress in the Children's Online Privacy Protection Act (COPPA), which prohibits Web sites from collecting information on children younger than 13 years without parental permission. Second, the official terms of service for many popular sites now mirror the COPPA regulations and state that 13 years is the minimum age to sign up and have a profile. This is the minimum age to sign on to sites such as Facebook and MySpace. There are many sites for preadolescents and younger children that do not have such an age restriction, such as Disney sites, Club Penguin, and others.

It is important that parents evaluate the sites on which their child wishes to participate to be sure that the site is appropriate for that child's age. For sites without age stipulations, however, there is room for negotiation, and parents should evaluate the situation via active conversation with their preadolescents and adolescents.

In general, if a Web site specifies a minimum age for use in its terms of service, the American Academy of Pediatrics (AAP) encourages that age to be respected. Falsifying age has become common practice by some preadolescents and some parents. Parents must be thoughtful about this practice to be sure that they are not sending mixed messages about lying and that online safety is always the main message being emphasized.

Pediatricians are in a unique position to educate families about both the complexities of the digital world and the challenging social and health issues that online youth experience by encouraging families to face the core issues of bullying, popularity and status, depression and social anxiety, risk-taking, and sexual development. Pediatricians can help parents understand that what is happening online is an extension of these underlying issues and that parents can be most helpful if they understand the core issues and have strategies for dealing with them whether they take place online, offline, or, increasingly, both.

Some specific ways in which pediatricians can assist parents include:

Advise parents to talk to their children and adolescents about their online use and the specific issues that today's online kids face.

Advise parents to work on their own participation gap in their homes by becoming better educated about the many technologies their youngsters are using.

Discuss with families the need for a family online-use plan that involves regular family meetings to discuss online topics and checks of privacy settings and online profiles for inappropriate posts. The emphasis should be on citizenship and healthy behavior and not punitive action, unless truly warranted.

Discuss with parents the importance of supervising online activities via active participation and communication, as opposed to remote monitoring with a “net-nanny” program (software used to monitor the Internet in the absence of parents).

In addition, the AAP encourages all pediatricians to increase their knowledge of digital technology so that they can have a more educated frame of reference for the tools their patients and families are using, which will aid in providing timely anticipatory media guidance as well as diagnosing media-related issues should they arise.

To assist families in discussing the more challenging issues that kids face online, pediatricians can provide families with reputable online resources, including “Social Media and Sexting Tips” from the AAP ( www.aap.org/advocacy/releases/june09socialmedia.htm ), 30   the AAP Internet safety site ( http://safetynet.aap.org ), 31   and the AAP public education site, HealthyChildren.org ( www.healthychildren.org/english/search/pages/results.aspx?Type=Keyword&Keyword=Internet+safety ), 32   and encourage parents to discuss these resources with their children. Pediatricians with Web sites or blogs may wish to create a section with resources for parents and children about these issues and may suggest a list of or links to social media sites that are appropriate for the different age groups. In this way, pediatricians can support the efforts of parents to engage and educate youth to be responsible, sensible, and respectful digital citizens.

Gwenn Schurgin O'Keeffe, MD

Kathleen Clarke-Pearson, MD

Deborah Ann Mulligan, MD, Chairperson

Tanya Remer Altmann, MD

Ari Brown, MD

Dimitri A. Christakis, MD

Holly Lee Falik, MD

David L. Hill, MD

Marjorie J. Hogan, MD

Alanna Estin Levine, MD

Kathleen G. Nelson, MD

Benard P. Dreyer, MD

Gilbert L. Fuld, MD, Immediate Past Chairperson

Victor C. Strasburger, MD

Michael Brody, MD

American Academy of Child and Adolescent Psychiatry

Brian Wilcox, PhD

American Psychological Association

Gina Ley Steiner

Veronica Laude Noland, [email protected]

This document is copyrighted and is property of the American Academy of Pediatrics and its Board of Directors. All authors have filed conflict of interest statements with the American Academy of Pediatrics. Any conflicts have been resolved through a process approved by the Board of Directors. The American Academy of Pediatrics has neither solicited nor accepted any commercial involvement in the development of the content of this publication.

The guidance in this report does not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

All clinical reports from the American Academy of Pediatrics automatically expire 5 years after publication unless reaffirmed,revised, or retired at or before that time.

American Academy of Pediatrics

RE: Social Media and Parenting

Social media is amongst one of the leading ways children, adolescents, and teens stay connected to the each other and the world around them. Ahn (2011) in the article “The Effect of Social Network Sites on Adolescents' Social and Academic Development” identifies the pros and cons of social media on children; the author highlights the needs for physicians and parents to control how children use social media. This dominating tool can sway the youthful generation for positive and negative influences. In my opinion, it is the sole responsibility of the parent to be actively monitoring the usage of underage children using technology as a means to stay connected to peers and the outside world. Marcus Barlow, program coordinator for the American Academy of Pediatrics Iowa Chapter, is one of those trying to find data that will arm parents with better information to make these decisions (Gowens, 2015). Parents need to gain control by learning their child’s “go to” networking sites and how they work. From a personal point of view, my son has a smartphone, which is monitored daily by me. His primary use for it is staying connected with family and friends through social media sites, such as Facebook (being number one), snap chat, youtube, and many others. It does affect him while being at the dinner table and even going to sleep at night. In which, I take the phone away at certain hours of the day. However, my niece uses social media to help teach her things, while her mother is at work. As sad as it may be, both parents need to be away for work more than they are home sometimes. Social media has taught my niece things like a morning routine, hair tips, and tricks, and even taking care of her menstruation (she didn’t even notify her parents, she turned to social media for help). On the contrary, is it permissible or safe that social media is raising our children? Unfortunately, we live in a world that both parents are forced to work to make ends meet. If both parents aren’t working today, they’re struggling to make ends meet. Social media is all over the place and hard to avoid. Most devices have “wifi” ability built in them, including TV’s and much more. In conclusion, Social media is amongst one of the leading ways children, adolescents, and teens stay connected to the each other and the world around them. There’s many studies going regarding the issues and effects social media has on our children, adolescents, and teens. There are pros and cons to this issue. References Ahn, J. (2011). The effect of social network sites on adolescents' social and academic development: Current theories and controversies. Journal of the American Society for information Science and Technology, 62(8), 1435-1445. Gowens, A. (2015, February 26). --> Health: Social media affects the teens, tween's physical and mental health | The Gazette. Retrieved from http://www.thegazette.com/subject/life/health-social- media-affects-the-teens-tweens-physical-and-mental-health-20150226

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Science News

Social media harms teens’ mental health, mounting evidence shows. what now.

Understanding what is going on in teens’ minds is necessary for targeted policy suggestions

Most teens use social media, often for hours on end. Some social scientists are confident that such use is harming their mental health. Now they want to pinpoint what explains the link.

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By Sujata Gupta

February 20, 2024 at 7:30 am

In January, Mark Zuckerberg, CEO of Facebook’s parent company Meta, appeared at a congressional hearing to answer questions about how social media potentially harms children. Zuckerberg opened by saying: “The existing body of scientific work has not shown a causal link between using social media and young people having worse mental health.”

But many social scientists would disagree with that statement. In recent years, studies have started to show a causal link between teen social media use and reduced well-being or mood disorders, chiefly depression and anxiety.

Ironically, one of the most cited studies into this link focused on Facebook.

Researchers delved into whether the platform’s introduction across college campuses in the mid 2000s increased symptoms associated with depression and anxiety. The answer was a clear yes , says MIT economist Alexey Makarin, a coauthor of the study, which appeared in the November 2022 American Economic Review . “There is still a lot to be explored,” Makarin says, but “[to say] there is no causal evidence that social media causes mental health issues, to that I definitely object.”

The concern, and the studies, come from statistics showing that social media use in teens ages 13 to 17 is now almost ubiquitous. Two-thirds of teens report using TikTok, and some 60 percent of teens report using Instagram or Snapchat, a 2022 survey found. (Only 30 percent said they used Facebook.) Another survey showed that girls, on average, allot roughly 3.4 hours per day to TikTok, Instagram and Facebook, compared with roughly 2.1 hours among boys. At the same time, more teens are showing signs of depression than ever, especially girls ( SN: 6/30/23 ).

As more studies show a strong link between these phenomena, some researchers are starting to shift their attention to possible mechanisms. Why does social media use seem to trigger mental health problems? Why are those effects unevenly distributed among different groups, such as girls or young adults? And can the positives of social media be teased out from the negatives to provide more targeted guidance to teens, their caregivers and policymakers?

“You can’t design good public policy if you don’t know why things are happening,” says Scott Cunningham, an economist at Baylor University in Waco, Texas.

Increasing rigor

Concerns over the effects of social media use in children have been circulating for years, resulting in a massive body of scientific literature. But those mostly correlational studies could not show if teen social media use was harming mental health or if teens with mental health problems were using more social media.

Moreover, the findings from such studies were often inconclusive, or the effects on mental health so small as to be inconsequential. In one study that received considerable media attention, psychologists Amy Orben and Andrew Przybylski combined data from three surveys to see if they could find a link between technology use, including social media, and reduced well-being. The duo gauged the well-being of over 355,000 teenagers by focusing on questions around depression, suicidal thinking and self-esteem.

Digital technology use was associated with a slight decrease in adolescent well-being , Orben, now of the University of Cambridge, and Przybylski, of the University of Oxford, reported in 2019 in Nature Human Behaviour . But the duo downplayed that finding, noting that researchers have observed similar drops in adolescent well-being associated with drinking milk, going to the movies or eating potatoes.

Holes have begun to appear in that narrative thanks to newer, more rigorous studies.

In one longitudinal study, researchers — including Orben and Przybylski — used survey data on social media use and well-being from over 17,400 teens and young adults to look at how individuals’ responses to a question gauging life satisfaction changed between 2011 and 2018. And they dug into how the responses varied by gender, age and time spent on social media.

Social media use was associated with a drop in well-being among teens during certain developmental periods, chiefly puberty and young adulthood, the team reported in 2022 in Nature Communications . That translated to lower well-being scores around ages 11 to 13 for girls and ages 14 to 15 for boys. Both groups also reported a drop in well-being around age 19. Moreover, among the older teens, the team found evidence for the Goldilocks Hypothesis: the idea that both too much and too little time spent on social media can harm mental health.

“There’s hardly any effect if you look over everybody. But if you look at specific age groups, at particularly what [Orben] calls ‘windows of sensitivity’ … you see these clear effects,” says L.J. Shrum, a consumer psychologist at HEC Paris who was not involved with this research. His review of studies related to teen social media use and mental health is forthcoming in the Journal of the Association for Consumer Research.

Cause and effect

That longitudinal study hints at causation, researchers say. But one of the clearest ways to pin down cause and effect is through natural or quasi-experiments. For these in-the-wild experiments, researchers must identify situations where the rollout of a societal “treatment” is staggered across space and time. They can then compare outcomes among members of the group who received the treatment to those still in the queue — the control group.

That was the approach Makarin and his team used in their study of Facebook. The researchers homed in on the staggered rollout of Facebook across 775 college campuses from 2004 to 2006. They combined that rollout data with student responses to the National College Health Assessment, a widely used survey of college students’ mental and physical health.

The team then sought to understand if those survey questions captured diagnosable mental health problems. Specifically, they had roughly 500 undergraduate students respond to questions both in the National College Health Assessment and in validated screening tools for depression and anxiety. They found that mental health scores on the assessment predicted scores on the screenings. That suggested that a drop in well-being on the college survey was a good proxy for a corresponding increase in diagnosable mental health disorders. 

Compared with campuses that had not yet gained access to Facebook, college campuses with Facebook experienced a 2 percentage point increase in the number of students who met the diagnostic criteria for anxiety or depression, the team found.

When it comes to showing a causal link between social media use in teens and worse mental health, “that study really is the crown jewel right now,” says Cunningham, who was not involved in that research.

A need for nuance

The social media landscape today is vastly different than the landscape of 20 years ago. Facebook is now optimized for maximum addiction, Shrum says, and other newer platforms, such as Snapchat, Instagram and TikTok, have since copied and built on those features. Paired with the ubiquity of social media in general, the negative effects on mental health may well be larger now.

Moreover, social media research tends to focus on young adults — an easier cohort to study than minors. That needs to change, Cunningham says. “Most of us are worried about our high school kids and younger.” 

And so, researchers must pivot accordingly. Crucially, simple comparisons of social media users and nonusers no longer make sense. As Orben and Przybylski’s 2022 work suggested, a teen not on social media might well feel worse than one who briefly logs on. 

Researchers must also dig into why, and under what circumstances, social media use can harm mental health, Cunningham says. Explanations for this link abound. For instance, social media is thought to crowd out other activities or increase people’s likelihood of comparing themselves unfavorably with others. But big data studies, with their reliance on existing surveys and statistical analyses, cannot address those deeper questions. “These kinds of papers, there’s nothing you can really ask … to find these plausible mechanisms,” Cunningham says.

One ongoing effort to understand social media use from this more nuanced vantage point is the SMART Schools project out of the University of Birmingham in England. Pedagogical expert Victoria Goodyear and her team are comparing mental and physical health outcomes among children who attend schools that have restricted cell phone use to those attending schools without such a policy. The researchers described the protocol of that study of 30 schools and over 1,000 students in the July BMJ Open.

Goodyear and colleagues are also combining that natural experiment with qualitative research. They met with 36 five-person focus groups each consisting of all students, all parents or all educators at six of those schools. The team hopes to learn how students use their phones during the day, how usage practices make students feel, and what the various parties think of restrictions on cell phone use during the school day.

Talking to teens and those in their orbit is the best way to get at the mechanisms by which social media influences well-being — for better or worse, Goodyear says. Moving beyond big data to this more personal approach, however, takes considerable time and effort. “Social media has increased in pace and momentum very, very quickly,” she says. “And research takes a long time to catch up with that process.”

Until that catch-up occurs, though, researchers cannot dole out much advice. “What guidance could we provide to young people, parents and schools to help maintain the positives of social media use?” Goodyear asks. “There’s not concrete evidence yet.”

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Office of the Surgeon General (OSG). Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory [Internet]. Washington (DC): US Department of Health and Human Services; 2023.

Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory [Internet].

Social media has both positive and negative impacts on children and adolescents.

The influence of social media on youth mental health is shaped by many complex factors, including, but not limited to, the amount of time children and adolescents spend on platforms, the type of content they consume or are otherwise exposed to, the activities and interactions social media affords, and the degree to which it disrupts activities that are essential for health like sleep and physical activity. 6 Importantly, different children and adolescents are affected by social media in different ways, based on their individual strengths and vulnerabilities, and based on cultural, historical, and socio-economic factors. 7 , 8 There is broad agreement among the scientific community that social media has the potential to both benefit and harm children and adolescents. 6 , 9

Brain development is a critical factor to consider when assessing the risk for harm. Adolescents, ages 10 to 19, are undergoing a highly sensitive period of brain development. 10 , 11 This is a period when risk-taking behaviors reach their peak, when well-being experiences the greatest fluctuations, and when mental health challenges such as depression typically emerge. 12 , 13 , 14 Furthermore, in early adolescence, when identities and sense of self-worth are forming, brain development is especially susceptible to social pressures, peer opinions, and peer comparison. 11 , 13 Frequent social media use may be associated with distinct changes in the developing brain in the amygdala (important for emotional learning and behavior) and the prefrontal cortex (important for impulse control, emotional regulation, and moderating social behavior), and could increase sensitivity to social rewards and punishments. 15 , 16 As such, adolescents may experience heightened emotional sensitivity to the communicative and interactive nature of social media. 16 Adolescent social media use is predictive of a subsequent decrease in life satisfaction for certain developmental stages including for girls 11–13 years old and boys 14–15 years old. 17 Because adolescence is a vulnerable period of brain development, social media exposure during this period warrants additional scrutiny.

• The Potential Benefits of Social Media Use Among Children and Adolescents

Social media can provide benefits for some youth by providing positive community and connection with others who share identities, abilities, and interests. It can provide access to important information and create a space for self-expression. 9 The ability to form and maintain friendships online and develop social connections are among the positive effects of social media use for youth. 18 , 19 These relationships can afford opportunities to have positive interactions with more diverse peer groups than are available to them offline and can provide important social support to youth. 18 The buffering effects against stress that online social support from peers may provide can be especially important for youth who are often marginalized, including racial, ethnic, and sexual and gender minorities. 20 , 21 , 22 For example, studies have shown that social media may support the mental health and well-being of lesbian, gay, bisexual, asexual, transgender, queer, intersex and other youths by enabling peer connection, identity development and management, and social support. 23 Seven out of ten adolescent girls of color report encountering positive or identity-affirming content related to race across social media platforms. 24 A majority of adolescents report that social media helps them feel more accepted (58%), like they have people who can support them through tough times (67%), like they have a place to show their creative side (71%), and more connected to what’s going on in their friends’ lives (80%). 25 In addition, research suggests that social media-based and other digitally-based mental health interventions may also be helpful for some children and adolescents by promoting help-seeking behaviors and serving as a gateway to initiating mental health care. 8 , 26 , 27 , 28 , 29

• The Potential Harms of Social Media Use Among Children and Adolescents

Over the last decade, evidence has emerged identifying reasons for concern about the potential negative impact of social media on children and adolescents.

A longitudinal cohort study of U.S. adolescents aged 12–15 (n=6,595) that adjusted for baseline mental health status found that adolescents who spent more than 3 hours per day on social media faced double the risk of experiencing poor mental health outcomes including symptoms of depression and anxiety. 30

As of 2021, 8th and 10th graders now spend an average of 3.5 hours per day on social media. 31 In a unique natural experiment that leveraged the staggered introduction of a social media platform across U.S. colleges, the roll-out of the platform was associated with an increase in depression (9% over baseline) and anxiety (12% over baseline) among college-aged youth (n = 359,827 observations). 32 The study’s co-author also noted that when applied across the entirety of the U.S. college population, the introduction of the social media platform may have contributed to more than 300,000 new cases of depression. 32 , 33 If such sizable effects occurred in college-aged youth, these findings raise serious concerns about the risk of harm from social media exposure for children and adolescents who are at a more vulnerable stage of brain development.

Limits on the use of social media have resulted in mental health benefits for young adults and adults. A small, randomized controlled trial in college-aged youth found that limiting social media use to 30 minutes daily over three weeks led to significant improvements in depression severity. 34 This effect was particularly large for those with high baseline levels of depression who saw an improvement in depression scores by more than 35%. 35 Another randomized controlled trial among young adults and adults found that deactivation of a social media platform for four weeks improved subjective well-being (i.e., self-reported happiness, life satisfaction, depression, and anxiety) by about 25–40% of the effect of psychological interventions like self-help therapy, group training, and individual therapy. 36

In addition to these recent studies, correlational research on associations between social media use and mental health has indicated reason for concern and further investigation. These studies point to a higher relative concern of harm in adolescent girls and those already experiencing poor mental health, 37 , 38 , 39 as well as for particular health outcomes like cyberbullying-related depression, 40 body image and disordered eating behaviors, 41 and poor sleep quality linked to social media use. 42 For example, a study conducted among 14-year-olds (n = 10,904) found that greater social media use predicted poor sleep, online harassment, poor body image, low self-esteem, and higher depressive symptom scores with a larger association for girls than boys. 43 A majority of parents of adolescents say they are somewhat, very, or extremely worried that their child’s use of social media could lead to problems with anxiety or depression (53%), lower self-esteem (54%), being harassed or bullied by others (54%), feeling pressured to act a certain way (59%), and exposure to explicit content (71%). 44

Unless otherwise noted in the text, all material appearing in this work is in the public domain and may be reproduced without permission. Citation of the source is appreciated.

• Cite this Page Office of the Surgeon General (OSG). Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory [Internet]. Washington (DC): US Department of Health and Human Services; 2023. Social Media Has Both Positive and Negative Impacts on Children and Adolescents.
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Impact of Social Media on Youth Mental Health (Discussion Paper)

Executive Summary

In a 2023 report, a Surgeon General’s Advisory declared a mental health crisis in the United States, warning of its devastating impact on youth and advising the country to act urgently to protect children and adolescents. 1 A shortage of adequate and accessible mental and behavioral health services exists in the United States, 2 with barriers to accessing proper care exacerbating the problem. Barriers include high costs, lack of or inadequate insurance coverage and patients' unawareness of how to access care. Additionally, there is a shortage of trained professionals, which the COVID-19 pandemic exacerbated. 3

Since 2011, the percentage of adolescents who experienced persistent feelings of sadness or hopelessness has continued to rise. 4 In 2021, 42% of high school students reported feeling sad or hopeless almost every day for two weeks, with 29% experiencing poor mental health during the past 30 days. To uncover and understand what impacts mental wellness in youths, social media has become an area of focus, with researchers exploring the impact social media is having on young people. 

It is important to note that environmental factors substantially impact adolescent brain development and contribute to mental wellness. Environmental factors range from those at the individual, family and community levels to others at the societal, state and national levels. Many environmental factors and their impact on development have been studied, and knowledge or predictability for impacts have been asserted by researchers, health care providers, caregivers and advocates — including an understanding of how factors impact mental wellness throughout and beyond adolescence. Social media is currently a prominent environmental factor being studied. While its impact is still understudied, we do know that it plays a significant role in the lives of many adolescents.

A 2022 Pew Research Center study found that approximately 95% of teens 13-17 years have access to a smartphone, and 90% have access to a desktop or laptop at home. 5 And the use of social media on those devices is ever-present in the lives of teens, as 97% report using at least one of the seven major online social media platforms. 6 YouTube continued to be the top platform among teens in 2023, with 93% reporting ever using the platform and 16% reporting they constantly visit or use the app or website. 7 Since TikTok broke onto the scene in 2018, it has become one of the top social media platforms for teens, with 63% saying they ever use the platform and 17% describing its use as almost constant. Instagram, Snapchat and Facebook are other top preferred social media platforms for most U.S. teens.

Social media platforms operate around algorithms designed to keep users engaged as long as possible and returning for more. By utilizing programming that intakes massive amounts of data around the activities and engagement of the user, such as “likes,” reactions to content, time spent on certain pages, etc., these platforms can continue to deliver content the user will find relevant or interesting. This algorithmic tailoring contributes to the positives and negatives of adolescents using these platforms, making social media a “double-edged sword.” 8

Access to social media can offer many benefits to adolescents, the most common being social connection. More than eight in ten adolescents describe social media as boosting their sense of connectedness. 9 An overwhelming majority report that social media makes them feel more connected to their friends (81%), helps them interact with a more diverse group of people (69%) and makes them feel they have people who support them through tough times (68%). 6 Most teens also associate their social media use with positive rather than negative emotions and feel included rather than excluded (71% versus 25%) and confident rather than insecure (69% versus 26%). 6

Social media can connect people with communities that provide positive and identity-affirming content, 1 which can be valuable to adolescents as they develop. Platforms cultivated around personal preferences and connections allow adolescents to join others in building relationships and growing existing ones. These platforms enable teens to receive social support and express their thoughts and feelings. 8 For youth, especially those in historically marginalized communities, these positive social media experiences can be crucial. Sharing photos, videos, status updates and written communication can help individuals experiencing loneliness and feelings of isolation.

In addition to creating and cultivating connections with others, teens use social media and the internet to access health information more readily, including information on mental health. Young people who may be suffering from mental health problems are spending time online searching for ways to understand and alleviate their symptoms. 10 Many adolescents may be reluctant to seek out help from others when they are experiencing symptoms out of fear of stigma, mistrust for the health care industry or even a lack of awareness. 11 Instead, they seek out information or support via a method they may trust or simply feel more comfortable accessing, such as social media and the internet. Social media offers a new opportunity to raise mental health awareness and share valuable resources. 9 By utilizing the instantaneous accessibility of social media, there are extensive possibilities to reach adolescents who were previously inaccessible. Access to social media offers the opportunity to reduce health and education disparities within communities that are otherwise unable to access those needed resources. 10

In-person interactions can be perceived as a casualty of increased time on social media, as adolescents’ daily interactions with friends are more likely to occur online. Teens have reported various reasons for lack of in-person socialization, including personal obligations (41%), other obligations of their friends (34%), difficulty finding transportation (32%), the ease of staying in touch online/by phone (33%) and parental restrictions (19%). 6 Most teens report that social media is somewhat important for having meaningful conversations with friends (69%), and utilizing social media is at least somewhat important for keeping up with friends on a day-to-day basis (77%). 12 It appears that online communication among teens supports “traditional” tasks of offline friendships. 10

When communities are created and cultivated, this connection can play an important role in the interpersonal support that helps youths develop and navigate what can sometimes be a very tumultuous time in their lives. When used in the service of connection, social media can support and even boost well-being.

Harms associated with social media include cyberbullying and harassment, exposure to content showing inappropriate and harmful behavior, exposure to predatory behavior and interactions, social comparison, receiving incorrect information and oversharing of personal information. 1 While these experiences are not exclusive to social media, they provide an easily accessible avenue for exposure. Many social media platforms are designed to allow for endless access to content and information. The amount of time youths engage with that content may impact their mental wellness and health. Research shows that individuals who spend three or more hours per day on social media can double the risk of poor mental health outcomes, including symptoms of depression and anxiety among children and adolescents.

How a young person engages with social media may also impact mental health. Researchers have termed two types of social media engagement: passive and active. 13 Passive use includes scrolling through content posted by social media friends or browsing news feeds or profiles — all without active interaction. This can result in upward social comparison, which can negatively affect well-being. Active use includes chatting, messaging or interacting with content and others, which can increase connectedness, social capital and positively affect well-being. The environmental factors, attitudes and behaviors surrounding an adolescent may play a role in how they interact with social media and offer an opportunity to better understand the link between social media and mental wellness.

Nearly half (46%) of U.S. teens 13-17 years report having experienced at least one form of cyberbullying, such as offensive name-calling (32%), spreading of false rumors about them (22%), receiving explicit images they didn’t ask for (17%), constantly being asked where they are, what they are doing or who they are with by someone other than a parent (15%), physical threats (10%) and having explicit images of them shared without their consent (7%). 14 Many teens (28%) reported having experienced multiple types of cyberbullying, with 31% of teens thinking their harassment or bullying had something to do with their personal attributes, such as gender (22%), racial or ethnic background (20%), sexual orientation (12%) or political views (11%).

White, Black and Hispanic teens do not statistically differ in having ever been harassed online, but specific types of online attacks are more prevalent among certain groups. Socioeconomic factors, such as household income, show that teens from households making less than $30,000 annually are twice as likely to be physically threatened online as teens living in households making $75,000 or more per year (16% versus 8%). 14 A wide majority (93%) of teens from all backgrounds and genders say that online harassment and bullying is a problem for people their age, with 53% saying it is a major problem and 40% saying it is a minor problem.

Along with the risks of online cyberbullying, there are also the risks of accessing content that promotes self-injurious behavior by depicting, discussing or generally normalizing it. 15 Negative peer experiences, such as cyber victimization, social exclusion and online conflict or drama, have consistently been associated with higher rates of self-harm and suicidal behavior and internalizing and externalizing problems.

Social comparison is a human trait that social media can exacerbate. Platforms create another dimension since social media does not exist in real time. Individuals engage in “selective self-presentation” 12 by presenting posts, images and content that are altered and manipulated to project a more idealized portrayal of themselves. There is no requirement on platforms for honest transparency of the person posting, which can mislead other users. Youth are at more risk of this presentation, as their brains are still vulnerable and routinely engage in social comparison. Part of teen development is seeking out social connection and validation, so content cultivated around comparison may lead some youth to “engage in negative social comparisons regarding their own accomplishments, abilities or appearance” and become a risk for mental wellness issues.

Addressing the Impact of Social Media on Youth Mental Health

U.S. Regulatory Efforts

The Children’s Online Privacy Protection Act of 1998 is a U.S. federal law with specific requirements for websites and online services to protect the privacy of users under 13 years. 16 These protective measures require sites to verify parental consent for the collection or use of personal information of young users, including seeking consent, displaying a privacy policy in any place where data is collected and outlining the legal responsibilities of a website operator, such as marketing, which targets children.

In 2022, the Kids Online Safety Act was introduced to the Senate with guidelines intended to protect minors using social media by requiring platforms to enact certain safeguards, restrict access to minors’ personal data and equip guardians with tools to supervise minors’ use of the platform. 17

In 2023, the bipartisan congressional bill, Protecting Kids on Social Media Act , was introduced. This bill aims to protect young users by placing mandates on social media companies requiring age verification and parental consent for users under 18 years. 18,19 The bill would also establish a pilot program to verify that the account user meets the age requirements/parental consent requirements by uploading a form of government-issued identification. If the bill is passed, a civil penalty will be enforced against social media companies that do not comply.

Many states have become more involved in regulating social media in some capacity. More than half of states in the U.S. have enacted bills, adopted resolutions or have pending legislation to protect young social media users, many of which are focused on age verification, parental consent and upholding federal regulations. 20

In 2023, a bipartisan effort was put forth by 33 states when a lawsuit was filed against Meta (formerly called the Facebook company) in the Northern District of California. 21 The lawsuit claims that Meta routinely collects data on its users, including children under 13 years, violating federal law. In early 2024, New York City designated social media as a public health hazard, comparing it with other public health hazards like tobacco and guns. 22

International Regulatory Efforts

The Children’s Code (i.e., Age Appropriate Design Code) is a United Kingdom-based internet safety and privacy code of practice by the Information Commissioner’s Office that falls under the Data Protection Act of 2018. 23 This code applies to any internet-based service likely to be accessed by anyone under 18 years, even if the intended audience is not youth under 18 years. While this code is U.K.-based, it does apply to any company that processes the personal data of U.K. children — whether they are a U.K. company or not. To conform with the code requirements, website code designers must implement safeguards that provide the highest level of privacy by default when children may use the sites. This includes not allowing access to other users' data, switching off geolocation tracking and restricting behavioral profiling, such as algorithmic curation or targeted advertising. This code does not apply to institutions processing information for educational purposes.

In 2023, the U.K. passed the Online Safety Act . This bill takes a zero-tolerance approach to protecting children and holding social media platforms responsible for the content hosted on their sites. 24 Under this new law, social media platforms will be expected to prevent illegal content from appearing on their sites or remove the content quickly to minimize the exposure, including self-harm content, thereby preventing children from accessing harmful and age-inappropriate content and providing parents and children clear and accessible ways to report problems online. This law will hold social media platforms legally liable to enforce the restriction of harmful or illegal content from appearing on sites through fines if they fail to follow through on these protective efforts.

The European Union has adopted the Digital Services Act Package , which aims to create safer digital spaces and protect users' rights while establishing a level playing field for businesses. 25 The act has two parts: the Digital Services Act and the Digital Market Act.

The DSA primarily focuses on online intermediaries and platforms, such as online marketplaces, social networks, content-sharing platforms, app stores and online travel/accommodation platforms. All platforms must publish their user numbers, and any with more than 45 million users will be designated as a very large online platform or a very large online search engine, requiring them to comply with the strictest rules under the DSA within four months of designation. 26 These requirements mean these VLOP/VLOSEs must identify, analyze and assess systemic risks linked to their services, such as illegal content; fundamental rights, such as freedom of expression or consumer protection and children’s rights; public security and electoral processes; and gender-based violence, public health, protection of minors and mental and physical well-being. Once identified, risks must be mitigated via an established internal compliance function and audited annually. The DSA also requires that vetted researchers are allowed “to access the platform data when the research contributes to the detection, identification and understanding of systemic risks in the EU.”

Research Efforts

Access to data collected by social media platforms may offer better insight for researchers hoping to learn more about the association between social media and mental health. Historically, however, data have not been made accessible to independent scientists. 27

Existing research about social media use by youths often is built on an overreliance on cross-sectional and correlational data 10 and does not examine social media use across youth development. 28 This makes it difficult to determine whether social media use leads to mental health issues or whether individuals with mental health issues are simply more vulnerable and likely to use the platforms in harmful ways. 10 Therefore, further research studying the longitudinal effects of social media use by youths is imperative to examine whether their use precedes and/or predicts mental health outcomes. 12 Research into social media use and discoveries about the causal relationship with mental health may not only help negate the potential harms to mental wellness but also allow for developing interventions for social media use to promote better mental health. 29

Regulatory Efforts on Transparency and the Challenge of Privacy Versus Freedom

Social media can be an asset for adolescents and their communities, but understanding how best to regulate it to protect users and mitigate potential harms is an ongoing debate at the state and national levels in the United States and internationally. Mental health and its effects are complex, with varying goals an individual might have during social media use served by different behavior patterns. In turn, these goals and behavior patterns are likewise produced by distinct patterns of use and outcomes. 9 Continued research is vital to understanding how users of all ages interact with social media. Answers to many research questions may lie in the data that social media companies collect on users and have been hesitant or unwilling to share. In recent years, the following legislation has been introduced to gain access to data for independent researchers to examine while still protecting the platform user, especially children and adolescents.

In 2022, the Platform Accountability and Transparency Act was introduced to provide independent researchers access to platform data via privacy-protected and secure pathways developed by the Federal Trade Commission to understand what information is being collected from users. 30

In 2021, the Social Media DATA Act called for social media platforms to provide information to academic researchers to better understand online targeted advertisements. 31 This work would be done under the supervision of the FTC to ensure any research with confidential data complies with consumers’ right to privacy.

In 2022, the Digital Services and Oversight and Safety Act allowed academic or non-profit researchers, certified by the FTC’s Office of Independent Research Facilitation, to access data to develop a deeper understanding of how social media platforms impact society while complying with the FTC’s established security requirements. 32

An ongoing challenge is finding the balance among three competing interests: protecting users’ personal privacy and wellness, protecting the company’s data privacy and protecting society’s freedom of speech concerns. The balance should include holding social media companies accountable throughout all stages of product development and ensuring they prioritize youth mental health and well-being in the use of their products while not imposing an overreach of government control on companies’ free market rights and users’ free speech rights. 33

When independent researchers are able to access and analyze existing social media data, there is a growing understanding of how users engage with social media, leading to better support for mental wellness and protection from the potentially harmful effects of social media.

Opportunities for Intervention

Since family physicians care for patients across their lifespan, they are ideal partners in supporting parental and guardian efforts to maintain and improve youth mental wellness. When interacting with adolescent patients, family physicians should routinely screen for mental health concerns. This should include not only time spent using social media but also assessing problematic and/or harmful use. 34 They can also help monitor an individual's mental wellness over long periods to assess how mental health fluctuates and consider individual environmental factors that may impact a youth patient. If a youth patient is presenting with symptoms of anxiety, depression, addiction, 1,35 body dysmorphia 36 or other concerning behaviors, it can be beneficial to assess for exposure to harmful social media interactions. 34 It can also be important to assess social media use that may benefit a patient, such as LGBTQ+ adolescents for whom social media use may provide critical support. 37

When physicians interact with adolescents and their parents or guardians, creating a “context of a therapeutic alliance” 15 is an effective way to establish communication or dialogue. This includes creating an environment for adolescent patients that is “open and nonjudgmental, elicits trust and emotional safety and offers a sense of inclusion and autonomy.” This approach has the bonus benefit of bolstering trust in the health care system for future encounters.

Motivational interviewing is one effective approach for family physicians to use when communicating with youths about other health concerns, and it may be an effective communication tool when discussing social media use in the exam room. This environment allows advocates to provide individualized support for young people experiencing mental wellness issues and provide individualized recommendations to interact with social media safely and healthily.

When communicating with parents or guardians, it can be important to explain why banning social media entirely would be ineffective. Young people have a biological drive to seek out social belonging, so prohibitive efforts around social media use may be impractical as teens can still find ways to access these platforms. Encouraging parents or guardians to discuss the benefits and harms of social media use with their teens and supporting them in teaching children critical thinking skills when reading online material can foster open communication about social media use and the content teens encounter. When teens have more robust social media literacy, they are less likely to suffer from the negative effects associated with social media. 15,38

Parents or guardians may not be able to control every piece of content children interact with on social media, but they can make efforts to model healthy social media practices for their teens. Doing so can be as simple as making active efforts to unfollow users or content that is idealized or overly edited and promotes harmful actions and refrain from interacting with content they would not want their child to spend time viewing. Setting aside designated time for the family/support system away from social media or devices altogether can allow the opportunity to develop and grow in-person social relationships.

While social media has the potential to be a great asset to the education and wellness of adolescents as they develop, it is also essential to consider the risks. As more data becomes accessible and research continues to improve, so will better insights into how social media impact young people. Each individual develops differently, with varying sensitivity to their environment and content consumed on social media. While controlling for each adolescent would be impossible, physicians, parents, guardians and policymakers can continually support young people using social media and advocate for their mental wellness.

1.      The U.S. Surgeon General’s Advisory. Social media and youth mental health. U.S. Public Health Services. Accessed March 5, 2024. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf

2.      American Academy of Family Physicians. Mental and behavioral health care services by family physicians (position paper). Accessed March 5, 2024. https://www.aafp.org/about/policies/all/mental-health-services.html

3.      National Council for Mental Wellbeing. Study reveals lack of access as root cause for mental health crisis in America. Accessed March 5, 2024. https://www.thenationalcouncil.org/news/lack-of-access-root-cause-mental-health-crisis-in-america/

4.      Centers for Disease Control and Prevention. Youth Risk Behavior Survey. Data summary & trends report. 2011-2021. Accessed March 5, 2024. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/YRBS_Data-Summary-Trends_Report2023_508.pdf

5.      Vogels EA, Gelles-Watnick R, Massarat N. Teens, social media and technology 2022. Pew Research Center. Accessed March 5, 2024. https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/

6.      Anderson M, Jiang J. Teens’ social media habits and experiences. Pew Research Center. Accessed March 5, 2024. https://www.pewresearch.org/internet/2018/11/28/teens-social-media-habits-and-experiences/

7.      Anderson M, Faverio M, Gottfried J. Teens, social media and technology 2023. Pew Research Center. Accessed March 5, 2023. https://www.pewresearch.org/internet/2023/12/11/teens-social-media-and-technology-2023/

8.      Keles-Gordesli B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth . 2019;25(4):1-15.

9.      Khalaf AM, Alubied AA, Khalef AM, Rifaey AA. The impact of social media on the mental health of adolescents and young adults: a systematic review. Cureus . 2023;15(8):e42990.

10.   Odgers CL, Jensen M. Adolescent mental health in the digital age: facts, fears and future directions. J Child Psychol Psychiatry . 2020;61(3):336-348.

11.   O’Reilly M, Dogra N, Hughes J, et al. Potential of social media in promoting mental health in adolescents. Health Promot Int . 2019;34(5):981-991.

12.   Nesi J. The impact of social media on youth mental health: challenges and opportunities. N C Med J . 2020;81(2):116-121.

13.   Orben A. Teenagers, screens and social media: a narrative review of reviews and key studies. Soc Psychiatry Psychiatr Epidemiol . 2020;55(4):407-414.

14.   Vogels EA. Teens and cyberbullying 2022. Pew Research Center. Accessed March 5, 2024. https://www.pewresearch.org/internet/2022/12/15/teens-and-cyberbullying-2022/

15.   Abi-Jaoude E, Naylor KT, Pignatiello A. Smartphones, social media use and youth mental health. CMAJ . 2020;192(6):E136-E141.

16.   National Archives and Records Administration. Children’s Online Privacy Protection Rule. Code of Federal Regulations. Federal Trade Commission. Accessed March 5, 2024. https://www.ecfr.gov/current/title-16/chapter-I/subchapter-C/part-312

17.   Congress.gov. S.3663 - Kids Online Safety Act. 117 th Congress (2021-2022). Accessed March 5, 2024. https://www.congress.gov/bill/117th-congress/senate-bill/3663/text

18.   Congress.gov. S.1291 - Protecting Kids on Social Media Act. 118 th Congress (2023-2024). Accessed March 5, 2024. https://www.congress.gov/bill/118th-congress/senate-bill/1291/text

19.   Social Media Victims Law Center. What is the Protecting Kids on Social Media Act? Accessed March 5, 2024. https://socialmediavictims.org/congress/protecting-kids-on-social-media-act/

20.   National Conference of State Legislatures. Social media and children 2023 legislation. Accessed March 5, 2024. https://www.ncsl.org/technology-and-communication/social-media-and-children-2023-legislation

21.   Office of the Attorney General for the State of California. Meta Multistate Complaint. Accessed March 5, 2024. https://oag.ca.gov/system/files/attachments/press-docs/FINAL%20Meta%20Multistate%20Complaint%2C%20N.D.%20Cal.%20%28REDACTED%2C%20CONFORMED%29.pdf

22.   The Official Website of the City of New York. Transcript: Mayor Adams lays out future-focused vision for working-class New Yorkers in third State of the City address. Accessed March 5, 2024. https://www.nyc.gov/office-of-the-mayor/news/068-24/transcript-mayor-adams-lays-out-future-focused-vision-working-class-new-yorkers-third-state

23.   Information Commissioner’s Office. Introduction to the Children’s Code. Accessed March 5, 2024. https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/childrens-information/childrens-code-guidance-and-resources/introduction-to-the-childrens-code/

24.   U.K. Public General Acts. Online Safety Act of 2023. Accessed March 5, 2024. https://www.legislation.gov.uk/ukpga/2023/50/pdfs/ukpga_20230050_en.pdf

25.   European Commission. The Digital Services Act package. Accessed March 5, 2024. https://digital-strategy.ec.europa.eu/en/policies/digital-services-act-package

26.   European Commission. DSA: very large online platforms and search engines. Accessed March 5, 2024. https://digital-strategy.ec.europa.eu/en/policies/dsa-vlops

27.   American Psychological Association. Health advisory on social media use in adolescence. Accessed March 5, 2024. https://www.apa.org/topics/social-media-internet/health-advisory-adolescent-social-media-use.pdf

28.   Coyne SM, Rogers AA, Zurcher JD, et al. Does time spent using social media impact mental health?: an eight year longitudinal study. Comput Human Behav . 2020;106160.

29.   World Health Organization. Guidelines on mental health promotive and preventive interventions for adolescents. Accessed March 5, 2024. https://iris.who.int/bitstream/handle/10665/336864/9789240011854-eng.pdf?sequence=1

30.   Congress.gov. S.5339 - Platform Accountability and Transparency Act. 117 th Congress (2021-2022). Accessed March 5, 2024. https://www.congress.gov/bill/117th-congress/senate-bill/5339/text

31.   Congress.gov. H.R.3451 - Social Media DATA Act. 117 th Congress (2021-2022). Accessed March 5, 2024. https://www.congress.gov/bill/117th-congress/house-bill/3451/text?r=35&s=1

32.   Congress.gov. H.R.6796 - Digital Services Oversight and Safety Act of 2022. Accessed March 5, 2024. https://www.congress.gov/bill/117th-congress/house-bill/6796/text

33.   Matthews LJ, Williams HJ, Evans AT. TheRANDBlog. Protecting free speech compels some form of social media regulation. RAND. Accessed March 5, 2024. https://www.rand.org/pubs/commentary/2023/10/protecting-free-speech-compels-some-form-of-social.html

34.   American Psychological Association. Health advisory on social media use in adolescence. Accessed March 5, 2024. https://www.apa.org/topics/social-media-internet/health-advisory-adolescent-social-media-use.pdf

35.   Bozzola E, Spina G, Agostiniani R, et al. The use of social media in children and adolescents: scoping review on the potential risks. Int J Environ Res Public Health . 2022;19(16):9960.

36.   Gupta M, Jassi A, Krebs G. The association between social media use and body dysmorphic symptoms in young people. Front Psychol . 2023;14:1231801.

37.   Berger MN, Taba M, Marino JL, et al. Social media use and health and well-being of lesbian, gay, bisexual, transgender, and queer youth: systemic review. J Med Internet Res . 2022;24(9):e38449.

38.   Dane A, Bhatia K. The social media diet: a scoping review to investigate the association between social media, body image and eating disorders amongst young people. PLOS Glob Public Health . 2023;3(3):e0001091.

(April 2024 BOD)

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