Skip to content
Frontiers
Search

Original Research

Frontiers in Developmental Psychology ·

Volume 4 - 2026 | https://doi.org/10.3389/fdpys.2026.1796593

Wanting more: adolescents' expectations for social media responses from peers

  • Celina K. Bowman-Smith1
  • Elizabeth S. Nilsen1*corresponding author
  1. Department of Psychology, University of Waterloo, Waterloo, ON, Canada
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

Associations between adolescents' social media use and wellbeing vary widely across studies, with motivation for social media use explaining some of the variability. However, little is known about adolescents' consideration of their motivations during the process of creating social media posts. Addressing this gap, we employed an experimental design wherein adolescents (13–16; N = 103) participated in a simulated Instagram task where they created posts and indicated how much they hoped to receive engagement, advice, support, or entertainment. Later, other adolescents viewed posts and rated what the original poster had hoped to receive. Additionally, adolescents completed measures of their socio-emotional characteristics. Together, we were able to capture the responses adolescents hoped to receive from others while posting messages, the degree to which other adolescents can detect the posters' wanted responses, and whether adolescent characteristics related to the types of responses posters hoped to receive. Results found that viewers' ratings of what posters hoped to receive correlated with what the posters themselves reported wanting (most strongly for support and advice). However, the magnitude of ratings differed with posters reporting that they hoped for more of the responses (engagement, advice, and support, entertainment) than the viewers perceived them to want. Adolescent characteristics, including empathy, mood, emotion regulation, peer relationships, as well as frequency of social media use, related to the responses they hoped to receive from others. Findings suggest a mismatch between social media posters' motivation and online viewers' perceptions, with variability in motivations partially explained by adolescents' socio-emotional characteristics.

Keywords

Introduction

Social media has become ubiquitous among adolescents. With most youth using multiple platforms daily (Pew Research Center, 2023), technology has transformed how adolescents communicate with parents, friends, acquaintances, and strangers (Janssen et al., 2025; Nesi et al., 2018; Subrahmanyam and Greenfield, 2008). Hundreds of studies have examined the impact of social media use on wellbeing in recent years, with mixed findings, methodological challenges, and varying effect sizes (Valkenburg, 2022). Much of this work has been limited by a reliance on the amount of time spent using social media, prompting calls for studies examining how and why teens engage online (Weinstein and James, 2022; Yang et al., 2021).

Adolescents report that they use social media for connection, entertainment, inspiration, and information (van der Wal et al., 2024). While this past work provides insight into general motivations for social media use, less is known about adolescents' motivations for sharing specific content, in-the-moment, as they create and post content, as well as how other users detect such motivations. Addressing this gap, we designed a behavioral task wherein adolescents created posts within a simulated Instagram platform and reflect on what responses they hoped to receive from others. To determine whether these “motivations” were perceived by others, adolescents later viewed the posts and indicated what responses they thought the poster hoped to receive. Seeking to understand the variability in online experiences (van der Wal et al., 2024), we also examined whether adolescents' socio-emotional characteristics (empathy, emotion regulation, affect, and peer experiences) related to their motivations and perceptions of motivations.

By capturing both posters' motivations and viewers' perceptions at the individual post level, this work provides insight into the degree of communicative transparency during social media use. Moreover, this work allows for understanding how adolescents with certain characteristics may be approaching the act of posting social media content with differing intentions. Given that adolescents use social media to meet social and emotional needs, understanding how they consider their own motivations and expectations allows for greater insight into the variability in experiences across youth.

Theories and frameworks of social media

Weinstein and James's (2022) three lenses for understanding adolescents' online experiences (i.e., digital, developmental, and ecological) provide a useful framework for contextualizing the present investigation. Starting with the digital lens, social media creates a distinct interpersonal context for adolescents. For instance, relative to offline interactions, social media has asynchronicity, permanence, cue absence, quantifiability, publicness, and visualness (Nesi et al., 2018; Weinstein and James, 2022). At a broad level, these features transform adolescents' social interactions with peers by increasing the frequency and immediacy of social experiences, amplifying social demands and expectations, altering the quality of interactions with peers, fostering opportunities for compensatory social behaviors, and lastly, providing opportunities for novel social behaviors (Nesi et al., 2018). Additionally, in-the-moment, they may affect how/why adolescents share information and how content, as well as the intention behind the content, is perceived by others—aspects examined in the current study.

Speaking specifically to the absence of social cues, online communication may be less inhibited and more ambiguous (Lapidot-Lefler and Barak, 2015; Suler, 2004). Thus, there may be a disconnect between what adolescents want from other users in response to their social media posts, and how viewers perceive and respond to these posts. Indeed, individuals tend to take an egocentric communicative approach online. For instance, undergraduates who composed email or face-to-face messages perceived their statements as highly transparent regardless of context, however, the gap between perceived and actual transparency was greatest for emails (Kruger et al., 2005). Additionally, compliance with requests communicated over email tends to be overestimated due to a lack of perspective-taking (Roghanizad and Bohns, 2017). Within text messages, senders' confidence in communicative success is unrelated to actual success (Holtgraves, 2021), with this disconnect greater for communication with friends than with strangers (Riordan and Trichtinger, 2017). Within the ambiguity of online communication, messages may be more subject to interpretive biases stemming from personal characteristics (e.g., social anxiety; Kingsbury and Coplan, 2016; Miers et al., 2020). Together this work suggests that online users seem to overestimate others' comprehension of their shared content and that communicative interpretation may relate to individuals' characteristics. However, there is a paucity of research exploring how users convey and perceive specific motivations on social media. Further, and related to the second lens (developmental) there is a lack of work investigating the specific motivations of adolescents when posting content, with this being a unique developmental stage for social media use (Politte-Corn et al., 2023).

Adolescents' developmental stage makes them particularly vulnerable to the compelling features of social media use, yet it is also a time when social interactions and identity development are crucial to adolescents' wellbeing (Brown and Larson, 2009; Erikson, 1968). Adolescents' general motivation for engaging in social media use is crucial for understanding the association between social media use and wellbeing (Yang et al., 2021). Within the last few years, there are an increasing number of studies examining adolescents' motivations for engaging with social media sites, such as information sharing, passing time, social interaction, escapism, social capital (Jarman et al., 2021); social interaction, participation, distraction, content-subscription and exploration, emotional support, and self-expression (Wong and McLellan, 2024); entertainment or boredom (Gingras et al., 2025); need for self-presentation, need to belong, need for social comparison, and need for entertainment (Dorčić et al., 2025); and coping, social connection, self-identify management, information seeking, and social recognition (Ketibian et al., 2026). While these studies provide important insight into why youth decide to engage in social media broadly, they do not capture the specific aspect of motivation we are examining in this study, namely, what adolescents hope/expect to receive after posting certain content. Social media provides immediate and quantifiable feedback, during a time when peer opinions have a strong influence (Weinstein and James, 2022). Social feedback received can be beneficial or detrimental depending on the nature of that feedback (Schreurs et al., 2024). Moreover, there are nuanced rules within social media, for instance, sharing negative emotions on social media can be viewed harshly by other users (Waterloo et al., 2018). Thus, it is important to understand adolescents' motivations for posting, specifically what sort of responses they want to receive, and the transparency of these motivations. As well, while adolescents may have certain motives when posting online, other adolescents may interpret their posted content differently, a premise examined in the present study.

Of course, adolescents will vary in how they anticipate/hope others will respond to their social media posts, which maps on to the third lens framing this work (ecological). In addition to positing that online communication is interconnected with various environmental and social systems, this lens suggests that individual characteristics influence how teens communicate online. Moreover, the differing effects of social media on wellbeing from adolescent to adolescent (Beyens et al., 2020) and relations between demographic/socio-emotional skills and online experiences (Arató et al., 2020), suggest that characteristics should be considered within social media research.

Adolescent socio-emotional characteristics

How youth communicate with others varies according to both situational and individual factors (Mewhort-Buist et al., 2020). Within our present study, we focused on socio-emotional characteristics that have been show previously to relate to online communicative behavior, asking the novel question as to whether they relate to what adolescents hope to receive from others as they generate social media posts.

Affective and cognitive empathy have both been implicated in adolescents' online behavior such as cyberbullying (Ang and Goh, 2010; Ouvrein et al., 2018; Zych et al., 2019). While empathy has been studied extensively in relation to harmful self-reported online communication (e.g., cybervictimization; Salem et al., 2023), few studies explore the role of empathy within more neutral peer exchanges. Further, a paucity of work examines the role of empathy within experimental paradigms, despite findings that self-reports of social media behavior are often inaccurate (Verbeij et al., 2021). One exception to this is work by Bowman-Smith et al. (2021) who, using a simulated Snapchat app, found that adolescents with greater affective empathy chose to send more prosocial comments. How empathy relates to the types of responses adolescents hope to receive from others or how they consider what others hope to receive remains unknown.

Emotion regulation (Thompson, 1994) encompasses both adaptive, such as cognitive reappraisal, and maladaptive strategies such as suppressing emotions (McMahon and Naragon-Gainey, 2018). In an adolescent sample, less adaptive emotion regulation skills related to more aggressive online communication (Baroncelli and Ciucci, 2014). Moreover, challenges with emotion regulation have been associated with excessive self-disclosure among adolescents (Marino et al., 2020). Adolescents' trait and state mood may too relate to their perceptions and behavior online. For instance, depressed adolescents have been found to use social media in both adaptive (e.g., social support) and maladaptive ways (e.g., social comparison) to regulate their negative affect (Radovic et al., 2017). Thus, there is reason to believe that variability in adolescents' emotion regulation abilities and mood may differentially relate to what adolescents hope to receive from others when posting online or determining the desired responses of other users.

Youths' experiences with peers have been found to shape how adolescents themselves communicate online (Nesi et al., 2018). For instance, adolescents who have been victims of cyberbullying are more likely to perpetrate cyberbullying themselves (Mishna et al., 2012). Thus, peer experiences (online, but also offline) may relate to expectations adolescents have for online communication as well as how their social media posts are perceived by others.

Current study

As youth increasingly turn to social media for social interaction, identity exploration, and self-expression, concerns have been raised about the impact on adolescents' development and wellbeing (Barry et al., 2017; Michikyan and Suárez-Orozco, 2016; Weinstein and James, 2022). Despite such concerns, gaps remain in our understanding of adolescents' social media use, with a call for fine-grained measures and experimental designs of adolescent social media use (Valkenburg, 2022). Accordingly, using a simulated social media app, we asked adolescents (aged 13–16), to generate social media posts and then indicate the extent to which they considered what response they hoped to receive from others, what the likelihood of receiving such a response would be, and how much of certain responses (engagement, entertainment, support, and advice) they wanted to receive from the viewers of their posts. We then showed the generated posts to different adolescents and asked them to rate how much of each response type they thought the poster wanted, as well as their confidence in their perceptions.

To understand the variability in adolescents' motivations and post content, we examined associations between the posters' socio-emotional characteristics and both their own and viewer ratings. This allows us to determine whether adolescent characteristics relate to the type of responses they hope to receive from others who see their social media posts as well as what others perceive they hope to receive.

Thus, using an in-the-moment behavioral experiment (which avoids biases associated with hindsight/recollection), we were able to capture the motivations/expectations adolescents hold at the individual post level of social media engagement. Given the experimental context, it is important to note that we are not capturing motivations for spontaneous social media posting, but rather the types of responses adolescents indicate they would like to receive once their post has been generated. By inviting adolescents to a second phase of the study, we were able to explore the important question as to whether posters' motivations are transparent to other viewers.

We anticipated that, in response to their posts, adolescents would report primarily wanting to receive engagement from others, given that likes and comments on social media serve as a proxy for adolescents' status and popularity (Blease, 2015; Martinez-Pecino and Garcia-Gavilán, 2019). We were unsure whether adolescents would feel as though others would respond in the way that they had hoped. On the one hand, we wondered whether adolescents may underestimate the limited cues within online spaces, thereby showing overconfidence that others would response as they hoped. However, on the other hand, adolescents' experience within online spaces may have taught them that responses do not always align with intentions. This said, we predicted a mismatch between posters' wanted responses and viewers' perceptions given that online users tend to overestimate the transparency of their communicative intentions (Kruger et al., 2005). We anticipated that there would be variability in what youth hoped to receive from others, with this variability partially accounted for by their characteristics, for instance, that negative affect may relate to increased desire for support (Radovic et al., 2017), greater social media use would facilitate greater confidence in receiving their wanted response, and negative peer experiences would relate to lower perceptions of receiving a wanted response (Ophir et al., 2019).

Method

Phase 1: participants and procedure

Adolescents aged 13–16 years old (N = 103) participated in Phase 1 of the study (Mage = 14.28 years; SD = 1.22 years), recruited through a lab database and the community within a mid-sized Canadian city. Parents reported on their children's gender (with the options of male, female, non-binary, or an open-text box where they could provide an alternative identity),1 with this question indicating the sample consisted of 59 girls, 40 boys, four individuals with non-binary identity). Most participants spoke English as their first language (94%). Ninety percent of parents had a college degree or higher. Within this sample, 21 adolescents were reported to have (some comorbid) neurodevelopmental disorders (n = 13) and mental health disorders (n = 13). Rates were consistent with expectations for a community sample (DSM-5-TR; American Psychiatric Association, 2022), except for autism that had slightly higher rates (n = 7). The general pattern of results remained when participants with neurodevelopmental disorders were removed, thus, all participant data was retained. Parents' open-ended responses regarding ethnicity, fell within categories of: multi-ethnic (n = 24), Canadian (n = 18), Caucasian (n = 18), Asian (n = 11), European (n = 9), South Asian (n = 7), Middle Eastern (n = 4), and Indigenous (n = 1).

We were interested in capturing all adolescents' responses, with their familiarity with social media being one predictor for their motivations. Thus, there was no eligibility criteria associated social media familiarity/use. Most adolescents reported owning a cellphone (83%) and having an Instagram account (60%), the latter showing comparable rates to large samples of US teens (Pew Research Centre, 2025). The majority of participants were daily social media users (79%), with 1–2 h per day being the most frequently reported amount of daily use (Figure 1).

Distribution of self-reported daily social media and instagram use. frequency of participant's self-reported daily social media use **(A)** and Instagram use **(B)**.
Figure 1. Distribution of self-reported daily social media and instagram use. frequency of participant's self-reported daily social media use (A) and Instagram use (B).

Procedure

Parents/caregivers were emailed a Qualtrics consent form and a demographic questionnaire. If consent was given, adolescents were invited to participate remotely in a self-guided study (approximately 45 min), including the social media task and questionnaires. Study procedures were approved through the University of Waterloo research ethics board.

Social media task

A simulated social media app was designed to recreate Instagram, wherein participants created eight posts about various topics, informed by what adolescents report posting about most frequently on social media (i.e., friends, family, emotions, and achievements; Pew Research Center, 2018). Participants were told that they would be creating 8 Instagram posts that would be posted for other teens their age to see—and that they should try to create posts that are similar to what they post or think they could post (with reassurance that they did not need to have used Instagram previously). Following the introduction, participants were provided a topic for each post with instructions and given an array of 25 emoji pictures to use for their posts (presented in two orders) to maintain participants' confidentiality (i.e., not using personal photos as the posts were shared with other adolescents). After all posts were created, each post was shown back to the participants, and they were asked questions about each post:

  • 1) How much insight they felt they had into what response they hoped to receive from other users. Specially, they were asked, “When making this post, do you have a sense as to what type of response you hope to get from other teens?” Adolescents responded using a 5-point Likert scale ranging from 1 (not at all) to 5 (to a great extent).
  • 2) How likely it would be for them to receive their wanted response from others, with the question: “Think of a response (or type of responses) you would most like to receive from other teens on this Instagram. Do you think you will receive a response that you want from other teens?” Adolescents responded using a 5-point Likert scale ranging from 1 (definitely not) to 5 (definitely).
  • 3) The extent to which they wanted specific types of responses from other users using a sliding scale ranging from 0 (I would not want this response at all) to 100 (I would very much want this response), including entertainment, engagement, support, and advice [informed by Rimé (2007). These options were informed by Rimé (2007) work on motivations for sharing life events with others as well as the emotional impact of receiving responses from others following sharing, such as empathy, support, attention, advice, or entertainment; Rimé, 2009]. The definition of each response type was displayed. An ‘other' textbox was also included to capture any additional wanted responses. Specially, support was described as “I want empathy, comfort, or help from others,” Engagement was described as, “I want to be likable to others (e.g., receiving likes/attention),” Advice was described as, “I want feedback or guidance from others,” and Entertainment was described as, “I want others to laugh/smile.”

Adolescent characteristics

Following the social media communication task, participants were asked to complete questionnaires assessing their socio-emotional characteristics, including empathy, mood, emotion regulation, and peer relationships, presented in a fixed order to ensure participants were exposed to identical stimuli (Carlson and Moses, 2001).

Empathy

The Empathy Questionnaire for Children and Adolescents (EmQue-CA; Overgaauw et al., 2017) is a 14-item questionnaire assessing empathy in youth aged 10- to 18-years-old. The EmQue-CA measures Affective Empathy (α = 0.65), Cognitive Empathy (α = 0.86), and Intention to Comfort (α = 0.73), using a 3-point Likert scale ranging from 1 (not true) to 3 (true). An empathy total score was calculated by averaging across the three subscales and demonstrated an acceptable internal consistency within the current sample (α = 0.78).

Emotion regulation & mood

The Emotion Regulation Questionnaire for Children and Adolescents (ERQ-CA; Gullone and Taffe, 2012) is a 10-item questionnaire assessing emotion regulation in youth 10- to 18-years-old, measuring Cognitive Reappraisal (i.e., adaptive strategy; six items) and Expressive Suppression (i.e., maladaptive strategy; four items), using a Likert scale from 1 (strongly disagree) to 5 (strongly agree). Items of each subscale were summed; however, items 9 and 10 (one from each subscale) were removed due to technical errors in the social media app resulting in more than 30% missing data. With items removed, the ERQ-CA demonstrated sound consistency for the subscales (both α = 0.80).

To assess affect, adolescents completed the short form of the Positive and Negative Affect Schedule for Children (PANAS-C-SF; Ebesutani et al., 2012). The PANAS-C-SF is a 10-item self-report questionnaire, measuring Positive Affect (5 items) and Negative Affect (5 items). Participants rated the frequency of each emotion in the past few weeks on a 5-point Likert scale ranging from 1 (not at all) to 5 (extremely). An affect composite was created by averaging positive affect and reverse-scored negative affect (α = 0.83).

Peer experiences

Adolescents' experiences with peers were measured using two questionnaires. The Social Networking Experiences Questionnaire (SNEQ; Dredge et al., 2015), is a 14-item questionnaire for adolescents assessing adverse online peer experiences. Adolescents' report of the frequency of experiences over the past 6 months on a 7-point Likert scale [1 (never) to 7 (daily)] was summed (α = 0.87). For a measure of offline peer relationships, we administered the Patient Reported Outcomes Measurement Information System (PROMIS) Pediatric Peer Relationships Scale, short-form (PR-SF; DeWalt et al., 2013), comprising of 8 items asking about the frequency of social experience over the past 7 days on a 5-point Likert scale [0 (never) to 4 (almost always)]. The items were summed, with higher scores reflecting more positive peer relationships (α = 0.93).

Social media use

As mentioned above, social media use was included as a control measure. A social media use composite was created by averaging adolescents reported daily time spent on social media and, due to the specific nature of our task, reported daily time spent on Instagram (r = 0.47, p < 0.001).

Phase 2: participants and procedure

Parents/youth who participated in Phase 1 were contacted for Phase 2. At the time, some participants had turned 17 years of age, expanding the age range (13–17-year-olds). As expected, there was attrition, resulting in 88 adolescents in Phase 2 (Mage = 14.34 years; SD = 1.25 years; 53 girls, 4 non-binary). Participants who completed both phases of the study vs. those who only participated in the first study did not differ on any of the demographic, socio-emotional, or motivation measures, ps > 0.07.

Procedure

Participants viewed others' posts via Qualtrics. Twenty ‘sets' of posts were created, each containing 4 posts from 10 different participants (i.e., 40 posts/set). The sets were created in a pseudorandom order, ensuring that participants did not view their own posts. A total of 800 social media (i.e., 8 posts from 100 adolescents) posts created in Phase 1 were included in Phase 2. Three participants' posts were not included due to promoting risky behavior (n = 1), incompletion (n = 1), or participation after the post sets were created for Phase 2 (n = 1). Each set of posts was shown to adolescents with 4 to 5 viewers rating each set of posts (i.e., each viewer rated 40 posts total). For each post, participants were asked to determine what type of response the original poster wanted (i.e., support, engagement, entertainment, advice; same definitions provided) and the extent to which they wanted each responses using a 0 to 100 slider, and asked to estimate the accuracy of their interpretations from 1 (definitely not) to 5 (definitely).

Viewers' perceptions of the posters' wanted responses and their perceived interpretation accuracy of responses were averaged across the 4 to 5 adolescent viewers. Thus, we created a score of what other adolescents felt the poster wanted that could be compared to the poster's self-reported wanted responses.

Results

Preliminary analyses

Social media posts

While an analysis of the social media post content was not a primary objective of this study, post content was reviewed at a descriptive level. Posts had median word lengths of 14 to 16 words. Two coders found that the valence of the posts [on a scale from 1 (very negative) to 5 (very positive)] ranged from 2.38 to 4.38 with a median of 3.5 (ICC =.95), and the depth of personal disclosure [on a scale from 1 (very little/nothing) to 3 (a lot)] ranged from 1 to 3, with a median of 1.62 (ICC = 0.81). Frequency coding, conducted through a Linguistic Inquiry and Word Count software (Pennebaker et al., 2022) identified top words used for each post type: achievement: finally, year, won, school, today; emotions: time, today, day, life, friends; family: family, love, fun, time, mom; friends: friend(s), love, best, fun, and people.

Missing data

Data were analyzed for missing values. The EmQue-CA and social media use questions had no missing values. The SNEQ had two missing values, and the PR-SF had one. Following the item removal, the ERQ-CA had four missing values, and the PANAS-C-SF had two missing values. Little's Missing Completely at Random (MCAR) test was not significant for the PANAS-C-SF (χ2 = 10.14, df = 14, p = 0.752) and thus was imputed using single stochastic regression imputation. Little's MCAR test was significant for the ERQ-CA (χ2 = 37.83, df = 20, p = 0.009), so this variable was not imputed. For the Phase 1 social media task, there were no missing data. In Phase 2, the Qualtrics survey sliders were used by participants to indicate how much of each response type was wanted—a total of 4 sliders per post. The slider was defaulted to the zero position (thus scored as zero when left in that position). However, if a participant left the slider in the default position for all response options for that post (i.e., they never moved the slider for any responses; n = 5), we recorded this as missing data.

Outlier analysis

No variables violated normality assumptions (i.e., |skew| ≤ 3; |kurtosis| ≤ 10). Several univariate outliers (±3SD) were detected [seven within questionnaires; four within response ratings (Phase 1); 12 within viewers' responses (Phase 2)] and were subsequently Winsorized. No multivariate outliers (as per Mahalanobis distance) were detected.

Correlation analyses

Preliminary analyses prior to conducting the full models were examined to understand associations between adolescent characteristics and responses on the social media task (Table 1).

Table 1. Descriptive statistics and bivariate correlations for individual characteristics and social media task (phase 1).

VariableMSD1234567891011121314
Individual characteristics
1. Age14.281.22–
2. Empathy2.350.320.07–
3. Negative Online Experiences1.330.39−0.10−0.14–
4. Peer Experiences21.807.32−0.010.37**−0.21*–
5. Affect14.232.87−0.100.32**−0.35**0.61**–
6. Cognitive Reappraisal16.264.210.100.33**−0.32**0.33**0.38**–
7. Expressive Suppression8.562.99−0.07−0.21*0.13−0.28**−0.41**−0.06–
8. Social Media Use2.571.680.16−0.070.36**0.13−0.15−0.160.04–
Social media ratings
9. Entertainment66.2220.050.030.23*−0.100.42**0.28**0.35**−0.21*0.09–
10. Engagement65.2118.960.040.30**0.050.28**0.030.21*−0.120.20*0.49**–
11. Support49.3920.50−0.110.15−0.020.160.160.22*−0.14−0.050.38**0.41**–
12. Advice46.1019.98−0.190.15−0.070.140.170.22*−0.17−0.160.41**0.25**0.73**–
13. Response insight3.400.820.070.48**0.110.28**0.100.01−0.20*0.28**0.26**0.48**0.130.14–
14. Response likelihood3.530.610.160.46**0.040.25*0.140.10−0.27**0.140.28**0.43**0.26**0.26**0.69**–

*p < 0.05; **p < 0.01.

Phase 1: what responses adolescents hope to receive when posting on social media

Analytic approach

In Phase 1, we examined how much insight adolescents felt they had into what they wanted to receive, whether they felt they would receive this response, and the degree to which they hoped to receive various responses. Next, we examined whether youths' socio-emotional characteristics related to their responses, controlling for age, gender, and social media use as per past work showing effects of these variables (Maksniemi et al., 2022; Svensson et al., 2022). For all characteristic analyses in Phase 1, multilevel modeling (MLM) was used with a maximum likelihood estimator in SPSS Statistics (Version 29; IBM SPSS Statistics, 2022; Nezlek, 2008; Snijders and Bosker, 2012). This approach was chosen given the hierarchical nature of the data whereby social media posts (level-1) were nested within participants (level-2). Analyses were considered adequately powered to estimate unbiased regression coefficients, standard errors, and variance components as more than 50 units were available at the participant-level (Maas and Hox, 2005; Paccagnella, 2011). Prior to analyses, continuous level 2-predictors were grand-mean centered. Of note, given that the post topics were not related to our main research questions, response type was collapsed across topics (see Supplementary material for post topic analyses).

An unconditional model was first conducted to determine the Intraclass Correlation (ICC) for each of the wanted responses, perceived insight, and perceived likelihood. The ICCs for support, engagement, advice, and entertainment were 0.28, 0.39, 0.30, and 0.39 respectively. The ICCs for perceived insight and perceived likelihood were 0.37 and 0.34.

To test the characteristic hypotheses, empathy, emotion regulation, affect, peer experiences, along with control variables of age, gender, and social media use, were added as level-2 fixed effects with a random intercept. Snijders and Bosker (2012) model specification recommendations were followed, and accordingly, non-significant predictors were trimmed from the final models to avoid overfitting. Of note, to explore whether gender was needed as a control variable, the models were first run with non-binary participants excluded (n = 4) from the analysis, given that the small number of youths in our sample would not allow for a meaningful interpretation. Given that gender was not a significant predictor in any of the individual characteristic analyses, data from non-binary youth were added back and included in all final models, which did not include gender as a predictor (N = 103).

Adolescents' reported insight into what response they want from others

Adolescents reported feeling moderate insight as to what type of responses they wanted for their social media posts (M = 3.40, SD = 1.22, Range: 1–5). To examine if adolescents' characteristics related to their perceived insight into what responses they want from others, an MLM model was conducted with insight as the dependent variable. In the final trimmed model, participants with greater empathy (B = 1.20, SE = 0.22, p < 0.001) and more social media use (B = 0.15, SE = 0.04, p < 0.001) reported having a greater sense as to what responses they want from others.

Adolescents' estimated likelihood of receiving the response they want

Adolescents reported that it was moderately likely they would receive the response they wanted from others (M = 3.53, SD = 0.94, Range: 1–5). To address whether adolescents' characteristics related to their perceived likelihood of receiving the response they wanted, an MLM model was conducted. In the trimmed model, participants with greater empathy (B = 0.81, SE = 0.17, p < 0.001) indicated they would be more likely to receive the responses they wanted from other teens. Also, participants with greater expressive suppression, considered a less adaptive emotion regulation strategy, indicated they would be less likely to receive the response they wanted from others, (B = −0.04, SE = 0.02, p = 0.044).

What responses adolescents want to receive from others

To examine what responses adolescents were endorsing as wanting, we first looked at the extent to which posters reported wanting to receive each of the responses (support, engagement, advice, entertainment; from 0 to 100 using a repeated-measures ANOVA (response type: support, engagement, advice, entertainment), collapsed across post topics (i.e., as they were prescribed and not of focus). Mauchly's Test revealed a violation for the main effect of response type, (χ2(5) = 38.38, p < 0.001). Thus, the Greenhouse-Geisser correction is reported to correct for violation of sphericity. There was a main effect of response type, F(2.45, 246.99) = 51.14, MSE = 268.30, p < 0.001, ηp2 = 0.34. Follow-up planned comparisons paired samples t-tests were conducted, with Bonferroni correction applied (six comparisons; p < 0.008). Adolescents reported wanting more engagement (M = 65.21, SD = 18.96) and entertainment (M = 66.22, SD = 20.05) than both support (M = 49.39, SD = 20.50; ps < 0.001) and advice (M = 46.10, SD = 19.98; ps < 0.001). There were no differences between participants' interest in receiving support or advice (p = 0.028), and between receiving engagement or entertainment (p = 0.607).

To examine how adolescent characteristics related to their ratings for each response type, four separate MLM models were conducted (Table 2).

Table 2. Multilevel models predicting what response posters' wanted from their socio-emotional characteristics.

Fixed effectPerceived insight
BSEdfRβ2
Intercept3.410.07102.00
Empathy1.20**0.22101.910.23
Social media use0.15**0.04102.040.12
Fixed effectPerceived likelihood
*B**SE**df**Rβ2*
Intercept3.530.05102.00
Empathy0.81**0.17101.860.19
Expressive suppression−0.04*0.02101.760.04
Fixed effectEngagement response
*B**SE**df**Rβ2*
Intercept65.061.74102.00
Empathy15.94**5.99102.000.06
Peer experiences0.92**0.31102.000.08
Affect−1.81*0.77101.930.05
Fixed effectEntertainment response
*B**SE**df**Rβ2*
Intercept66.201.76101.99
Peer experiences0.94**0.26102.600.12
Cognitive reappraisal1.19*0.46101.930.06

Bs represent the unstandardized regression coefficients. Continuous predictors were grand-mean centered. Effect sizes were estimated using semi-partial R2, whereby 0.10 = small effect, and 0.50 = large effect.

*p < 0.05; **p < 0.01.

Entertainment. With all predictors entered, cognitive reappraisal and offline peer relationships emerged as significant predictors of entertainment. In the trimmed model, adolescents with better cognitive reappraisal skills, considered an adaptive emotion regulation strategy (B = 1.19, SE = 0.46, p = 0.011) and more positive peer relationships (B = 0.94, SE = 0.26, p < 0.001) reported wanting others to be more entertained (i.e., smile/laugh) by their posts.

Engagement. With all predictors entered, empathy, affect, and offline peer relationships, emerged as significant predictors of engagement. In the trimmed model, participants with greater empathy (B = 15.94, SE = 5.99, p = 0.009) and more positive peer relationships (B = 0.92, SE = 0.31, p = 0.004) reported wanting to receive more engagement from their posts. Participants with more negative positive affect also reported wanting more engagement in response to their posts (B = −1.81, SE = 0.77, p = 0.021).

Support. No predictors emerged as significant for predicting the degree participants wanted support in response to their posts (ps > 0.081).

Advice. With all predictors entered, age emerged as a significant predictor of the degree participants wanted advice. However, when the non-significant predictors were trimmed, age was not significant (B = −3.09, SE = 1.59, p = 0.055).

Phase 2: viewers' perception of what posters' wanted as a response

Analytic approach

In Phase 2, adolescents viewed 40 social media posts and rated the degree of each response type they felt the poster wanted and their confidence in their ratings. Thus, we could examine how they felt about their ability to accurately perceive what type of response posters wanted, and whether the degree of confidence varied by their own characteristics.

With respect to perceptions of specific response options, recall that 4–5 adolescent viewers rated each social media post as to how much the poster wanted to receive entertainment, engagement, advice, and support (from 0 to 100). Thus, we were able to create an average score (for each post) that reflected adolescents' perception of the degree to which posters wanted each response. We analyzed ratings across response types using a repeated measures ANOVA to determine what response types are perceived as being most desired and then examined each post's average score in relation to the posters' characteristics to determine whether the posters' demographic and socio-emotional characteristics related to how their posts were being perceived by others.

Adolescent viewers' confidence in determining what response posters wanted

Across the 40 posts each adolescent rated, adolescent viewers perceived they were probably accurate (M = 3.69, SD = 0.90, Range: 1–5) in their interpretations of the posters wanted responses. With respect to characteristics, with all predictors entered into the MLM model (ICC = 0.31), peer relationships emerged as significant. In the trimmed model, viewers with more positive peer relationships (B = 0.02, SE = 0.01, p = 0.002, RRβ2 = 0.11) felt they were more accurate in their interpretations of what posters wanted as a response.

Adolescent viewers' ratings of what posters' wanted from their social media posts

We conducted a repeated-measures ANOVA on the averaged score of adolescent viewers' perceptions of what response the poster wanted. Mauchly's Test revealed a violation for the main effect of response type (χ2(5) = 142.75, p < 0.001) and the Greenhouse-Geisser correction is reported. There was a main effect of response type, F(1.63, 162.80) = 149.62, MSE = 158.87, p < 0.001, ηp2 = 0.599. Follow-up planned comparisons paired samples t-tests were conducted, with Bonferroni correction (p < 0.008). Viewers perceived that posters wanted more engagement (M = 49.46, SD = 10.10) and entertainment (M = 43.60, SD = 10.58) than support (M = 32.69, SD = 10.80; ps < 0.001) or advice (M = 24.09, SD = 8.14; ps < 0.001) for their posts. Further, viewers reported that posters wanted more support than advice (p < 0.001) and more engagement than entertainment (p < 0.001) in response to their posts.

Next, we examined whether the characteristics of posters' (Phase 1) were associated with adolescent viewers' perceptions of what responses posters wanted on their social media posts (Phase 2), using bivariate correlations (Table 3) and linear regression analyses. The models for entertainment, support, and advice were not significant (ps > 0.607), suggesting that the posters' characteristics did not relate to the degree to which others viewed their interest in receiving those responses. The model for engagement was significant, R2 = 0.24, F(9, 87) = 3.05, p = 0.003, and age emerged as a significant predictor. In the trimmed model, the older posters were, the less engagement viewers perceived they wanted, B = −3.40, SE =0.76, p < 0.001.

Table 3. Bivariate correlations for posters' individual characteristics (phase 1) and viewers' perceptions (phase 2).

VariableMSDAgeEmpathyOnline experiencesPeer experiencesAffectCognitive reappraisalExpressive suppressionSocial media use
Entertainment43.6010.58−0.19−0.010.05−0.01−0.000.030.04−0.11
Engagement49.4610.10−0.41**−0.06−0.01−0.010.120.13−0.000.02
Support32.6910.80−0.13−0.020.03−0.01−0.02−0.01−0.040.04
Advice24.098.14−0.090.040.02−0.060.000.01−0.12−0.08
Perceived accuracy3.690.22−0.100.17−0.080.100.190.14−0.03−0.07

**p < 0.01.

How well do adolescent viewers perceive what posters' want as a response? phase 1 and 2

As a final step, we wanted to compare what posters indicated they wanted in response responses with viewers' perceptions of what posters wanted. We approached this comparison in two ways: comparing the magnitude of ratings for each response type and examining the association between the ratings.

First, a 2 (rater type: poster, viewer) × 4 (response type: entertainment, engagement, support, advice) repeated measures ANOVA was conducted. The dependent measure was how much (from 0 to 100) of each response was wanted as reported by posters (Phase 1) or perceived by receivers (averaged across 4–5 viewers; Phase 2). Mauchly's Test revealed a violation for the main effect of response type (χ2(5) = 64.36, p < 0.001) and the interaction (χ2(5) = 50.04, p < 0.001). As such, the Greenhouse-Geisser correction is reported. A main effect of rater type emerged, F(1, 99) = 162.08, MSE = 460.88, p < 0.001, ηp2 = 0.62. Across all response types, original posters reporting wanting a greater degree of responding (M = 56.81, SE = 1.53) than viewers perceived they wanted (M = 37.48, SE = 0.59). A main effect of response type also emerged, F(2.10, 207.88) = 133.95, MSE = 249.76, p < 0.001, ηp2 = 0.58. These main effects were qualified by a significant interaction of rater type by response type, F(2.35, 232.80) = 4.48, MSE = 169.88, p = 0.009, ηp2 = 0.04. However, when each response type was examined separately, results are consistent with the main effect, namely, that adolescents hope to entertain others [t(99) = 10.76, p < 0.001], receive more engagement [t(99) = 7.59, p < 0.001], receive more support [t(99) = 7.53, p < 0.001], and receive more advice [t(99) = 11.91, p < 0.001], than the adolescent viewers thought they wanted (Figure 2).

What posters want to receive as a response and viewers' perceptions of what posters want. Comparison of what posters report wanting as a response (Phase 1) and viewers' perceptions of posters' wanted responses (Phase 2).
Figure 2. What posters want to receive as a response and viewers' perceptions of what posters want. Comparison of what posters report wanting as a response (Phase 1) and viewers' perceptions of posters' wanted responses (Phase 2).

Second, while the ANOVA allowed us to explore the magnitude of discrepancy between posters' wanted responses and viewers' perceptions, we also conducted bivariate correlations to determine the associations between posters' and viewers' perceptions for each type of response to see if there was an association and whether the strength of the association differed by response type. Engagement and entertainment showed significant, but weak correlations (r = 0.24, 0.22, respectively, ps < 0.001); support and advice showed significant, moderate correlations (r = 0.43, 0.37, respectively, p < 0.001). Fisher's z-test was used to compare the strength of the correlation coefficients, showing the associations between poster and viewer ratings were significantly stronger for support and advice than for entertainment and engagement (ps < 0.001) Thus, while support and advice are desired less often (as per findings above), when posters are interested in receiving them, these interests are better detected by viewers.

Discussion

Learning to navigate the nuances of one's social environment is an important developmental competency during adolescence (Brown and Larson, 2009). This developmental task is increasingly complex in a world of constant connectivity, amplified social demands, and unwritten rules of social media use (Orben and Dunbar, 2017). Seeking to understand how adolescents approach social media and what they want from those on the other side of the screen, we created a simulated Instagram platform that examined (a) what responses adolescents want to receive from other teens when posting on social media, (b) viewers' perceptions what the posters' hoped to receive from their social media posts, (c) how adolescents' characteristics relate to what they hope to receive and others' perceptions of their posts, and (d) a comparison between what posters' want to receive from others and what viewers think they want. Together this work allows for understanding the motivations of adolescents as they communicate through social media.

What responses adolescents hope to receive from others when posting on social media

Adolescents, on average, reported having moderate insight into the responses they wanted on their posts and that it was moderately likely they would receive them. However, ratings varied by adolescent characteristics: adolescents with more social media use rated themselves as having greater insight into what they hoped to receive from others, potentially as increased experience with the act of posting may provide adolescents with the opportunity to reflect on what they are hoping to receive from others and/or the norms associated with social media behavior. Additionally, adolescents with greater empathy reported both more insight and more likelihood of receiving their desired response, possibly due to generally having a greater sense of their own internal states, including motivations and expectations (Guttman and Laporte, 2002; Moriguchi et al., 2007).

With respect to specific responses, adolescents reported wanting engagement and entertainment more than advice and support. There were no differences between participants' interest in support vs. advice, and engagement vs. entertainment. This pattern is consistent with previous work showing that engagement from posts is highly valued by youth (Sherman et al., 2016), however, as noted in our findings, adolescents may simultaneously hold several motivations for posting on social media. Another response adolescents reported wanting when posting was entertaining others by making them laugh and smile, which recent work found is a common motivation for adolescent social media use (van der Wal et al., 2024). Viewed through the developmental lens, finding that engagement and entertainment are the most desired responses is consistent with expectations for this developmental stage, namely that peer relationships, social status, and peer influence are extremely salient in adolescence (Brown and Larson, 2009). Adolescents had less interest in receiving support and advice from other teens. While the findings are consistent with a recent report as to why adolescents use Instagram in general (i.e., for entertainment; Pew Research Centre, 2026), it is likely the case that the pattern of findings are influenced by the experimental context in which the ‘other teens' were unknown to participants. That is, they may be less likely to seek support/advice from unknown viewers (vs. close contacts). To extend this work, it would be useful to explore motivations for posting with private friend networks vs. broader audiences and/or inquire as to how personal or connected to the post adolescents felt to see if this affects their motivations. As well, future studies could use experience sampling methods (van der Wal et al., 2024) to track adolescents' motivations at several time intervals within days/weeks to examine how motivations and expectations may fluctuate.

Adolescent characteristics and what they hope to receive from others

Adolescents' characteristics were associated with the degree to which they hoped to receive certain responses from others. First, adolescents with more positive peer relationships and more adaptive emotion regulation skills indicated greater interest in entertaining others (i.e., laughs/smiles) with their posts. The entertainment response is somewhat different from the others in that it focuses more on a poster wanting to provide the viewer with something more than receiving something in the response. Thus, it may be the case that adolescents who report positive relationships and have good emotion regulation are motivated to use social media as a way to create positive experiences for others. Moreover, given the value of using humor in creating social connections (Dunbar et al., 2021), it may be the case that individuals with these characteristics are more likely to use social media to affiliate with others.

Second, adolescents with greater empathy and positive peer relationships wanted more engagement on their posts. Previous work finds that social media users with greater trait empathy are more likely to engage with others' social media content (Mayshak et al., 2017), and as such may hold these communicative expectations for other users when posting their own content. Youth who engage in honest self-presentation online were found to have greater positive peer relationships which in turn facilitated more positive engagement online (Tian et al., 2024), which may reinforce wanting more engagement from peers given users' expectations that social feedback and engagement is likely to be positively valenced from experience. However, we also found that adolescents with greater negative affect wanted more engagement. Thus, it may also be the case that adolescents post on social media with hopes that engagement from others could regulate their negative affect, consistent with previous findings suggesting smartphones as a mood modification tool for adolescents (Minich and Moreno, 2024).

Interestingly, we did not find that, generally, the characteristics of the poster related to the types of responses that others perceived them wanting (with the exception of age). Thus, it seems that adolescents' characteristics are related to their own desires/expectations when posting as opposed to content of the posts (from which their motivations could be gleaned by others).

Viewers' perception of what posters' want from their social media posts (phase 2)

Adolescent viewers perceived that posters hoped to receive engagement and entertainment more than support and advice. Moreover, viewers reported that posters wanted support more than advice and engagement more than entertainment in response to their posts. In general, viewers indicated that they were probably accurate in their interpretations of what the poster wanted as a response to their posts, with adolescents who had better peer relationships showing more confidence in their perceptions.

We compared the ratings of the adolescent posters to the perceptions of the viewers for each social media post to examine whether what adolescents hope to receive from others is evident to those who view their posts. Significant associations between poster and viewer ratings were found, suggesting that the interests of the poster are coming through for viewers via social media posts, particularly when the poster is interested in receiving support and advice. However, the study findings revealed that across all response options, posters indicated more strongly wanting the response than viewers perceived posters wanted. Thus, similar to other work examining online communication (albeit looking at communicative intent; Kruger et al., 2005), our findings suggest a discrepancy between poster and viewer. A recent study found that while the inclusion of emojis does lead to greater communicative success, senders still significantly overestimated how likely receivers would be to interpret their conveyed meanings correctly (Holtgraves, 2024). This gap between what posters hope to receive and what others perceive they want may mean that within the process of posting via social media, there are expectations that are not met and/or feelings of dissatisfaction/disappointment.

Together these findings provide a valuable foundation from which future work can be developed, for instance, examining whether the degree of difference between what a poster hopes to receive and what others perceive they want is related to individual wellbeing/ill-being following social media use. Moreover, these findings could be extended to examine how perceptions of what someone hopes to receive as a response translates to actual responses (i.e., asking how often adolescents actually provide the type of response they think others' want) and what factors affect such an association.

Limitations and future directions

While this multimethod work has key strengths, such as asking adolescents to both create and view each others' social media posts within a simulated social media app and asking for adolescents' insight/motivation for each individual post, there are limitations that warrant consideration. First, we prompted adolescents with topics to provide some structure for youth during the study, drawing from most common topics for social media (Pew Research Center, 2018), and allowing for examination of adolescents' motivations and perceptions of such motivations in a controlled context. However, this did not allow us to explore motivations behind choosing to post in the first place or posts created spontaneously. As noted above, studies using different methodologies (e.g., experience sampling) could be useful to understand adolescents' motivations/expectations/reactions as they navigate social media choices in their everyday lives. Further, the use of emoji pictures in the posts, used to protect anonymity (of participants and others in their lives), limited the ecological validity. Additionally, as noted above, the teens who were viewing the posts were unknown to the participants, which may have influenced both what adolescents posted as well as the types of expectations they held for responses (e.g., less advice/support). With respect to adolescent characteristics, our focus was on socio-emotional factors, however, there are other individual differences, that may relate to what adolescents hope to receive in response to posts or how motivations of others are interpreted. For instance, characteristics such as social anxiety relate to online interpretative biases and avoidant online behavior (Miers et al., 2020), and actively open-minded thinking relates to more accurate online inferences and less frequent, but more thoughtful social media posts (Carpenter et al., 2018). As well, it would be useful for future work to examine characteristics such as sexual orientation and gender identity, including gender minority status, as past research shows that these characteristics relate to general motivations for social media use (Cormier et al., 2026; López-Sáez et al., 2024; Manago et al., 2023). Moreover, as our sample was relatively socioeconomically advantaged (in terms of parental education), it is important to note that findings may not generalize to other population groups, including those who have different socio-economic backgrounds, as per work demonstrating that lower socioeconomic status relates to increased negative experiences on social media (Skogen et al., 2022) as well as strengthened association between social media behavior and lower levels of wellbeing (Celik and Gracia, 2026). Related, while the rate of Instagram use was comparable to recent surveys (Pew Research Centre, 2026), we cannot be certain results would generalize to populations with different digital use profiles.

Conclusion

Emerging theories and models of social media use call for greater understanding of youths' characteristics to understand social media experiences as well as less reliance on self-reported online behavior in favor of more ecologically valid methodologies (Beyens et al., 2020; van der Wal et al., 2024). In two-phases of an experimental task, the present work investigated the types of responses adolescents hoped to receive when posting on a simulated social media app (Instagram) and whether other adolescents perceived such motivations. Adolescents indicated they wanted engagement and entertainment more than advice or support when posting on social media. While there were associations between the ratings of adolescent posters and viewers, adolescents who viewed the posts felt that the poster wanted less of all responses. Thus, it may be the case that what adolescents hope or expect to receive from others after they post on social media is less than what is provided by users. Speaking to the variability in social media experiences, adolescents' socio-emotional characteristics (i.e., empathy, emotion regulation, and peer relationships) as well as their time spent on social media, related to what they hoped to receive from others' responses, but not to how others perceived their posts.

Together, this work provides input for models of adolescent social media use (Yang et al., 2021) by highlighting the various hopes and expectations adolescents have when posting online (and disconnect with how their posts are viewed), as well as the characteristics that relate to such hopes. Moreover, findings from this work suggest directions for practical recommendations, for instance that social media literacy should include information regarding the mismatch between expectations and perceptions, such that the unique (often ambiguous) interpersonal features of social media (Nesi et al., 2018) are understood, and possible communicative pitfalls avoided. With this understanding youth may be better able to, in-the-moment, detect and regulate their own expectations regarding what others may provide during social media. Understanding how different communicative contexts are more/less able to meet social needs is important, particularly given work showing that expectations for different communicative channels differ from the actual benefit (Kumar and Epley, 2021). As well, given the salience of online communication within adolescent lives, clinicians who work with adolescents could routinely ask about the online behaviors that pose greater risks for adolescent wellbeing (e.g., upward social comparisons; Yang et al., 2021) as well as inquiring about adolescents' specific motivations for posting content, expectations for responses, and reactions to actual responses to generate a nuanced understanding of social (met/unmet) needs.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fdpys.2026.1796593/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Sarah Domoff, University at Albany, United States

Reviewed by: Miguel Ángel López-Sáez, Rey Juan Carlos University, Spain

Jessica Hamilton, Rutgers, The State University of New Jersey, United States

Footnotes

We recognize this question conflated gender and biological sex. While an open-text was provided as is recommended, future collection of information about gender identity should follow recent guidelines (e.g., López-Sáez, 2025).

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was funded through a Social Sciences and Humanities Research Council Insight Grant awarded to EN.

Data availability

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

Ethics approval

The studies involving humans were approved by University of Waterloo Research Ethics Board. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants' legal guardians/next of kin.

Author contributions

CB-S: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing. EN: Funding acquisition, Supervision, Writing – original draft, Writing – review & editing.

References

  1. American Psychiatric Association (2022). Diagnostic and Statistical Manual of Mental Disorders, 5th Edn. Washington, D.C: American Psychiatric Association. doi: doi:10.1176/appi.books.9780890425787
  2. Ang R. P., Goh D. H. (2010). Cyberbullying among adolescents: the role of affective and cognitive empathy, and gender. Child Psych. Hum. Dev. 41, 387 – 397. doi: doi:10.1007/s10578-010-0176-3
  3. Arató N., Zsidó A. N., Lénárd K., Lábadi B. (2020). Cybervictimization and cyberbullying: the role of socio-emotional skills. Front. Psych. 11: 248. doi: doi:10.3389/fpsyt.2020.00248
  4. Baroncelli A., Ciucci E. (2014). Unique effects of different components of trait emotional intelligence in traditional bullying and cyberbullying. J. Adolesc. 37, 807 – 815. doi: doi:10.1016/j.adolescence.2014.05.009
  5. Barry C. T., Sidoti C. L., Briggs S. M., Reiter S. R., Lindsey R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61, 1 – 11. doi: doi:10.1016/j.adolescence.2017.08.005
  6. Beyens I., Pouwels J. L., van Driel I. I., Keijsers L., Valkenburg P. M. (2020). The effect of social media on well-being differs from adolescent to adolescent. Sci. Rep. 10: 10763. doi: doi:10.1038/s41598-020-67727-7
  7. Blease C. R. (2015). Too many ‘friends,' too few ‘likes'? evolutionary psychology and ‘Facebook depression'. Rev. Gener. Psychol. 19, 1 – 13. doi: doi:10.1037/gpr0000030
  8. Bowman-Smith C. K., Sosa-Hernandez L., Nilsen E. S. (2021). The other side of the screen: the impact of perspective-taking on adolescents' online communication. J. Adolesc. 92, 46 – 56. doi: doi:10.1016/j.adolescence.2021.08.006
  9. Brown B. B., Larson J. (2009). “Peer relationships in adolescence,” in Handbook of Adolescent Psychology: Contextual Influences on Adolescent Developmen t, 3rd Edn. eds. R. M. Lerner and L. Steinberg (Hoboken, NJ: Wiley), 74 – 103. doi: doi:10.1002/9780470479193.adlpsy002004
  10. Carlson S. M., Moses L. J. (2001). Individual differences in inhibitory control and children's theory of mind. Child Dev. 72, 1032 – 1053. doi: doi:10.1111/1467-8624.00333
  11. Carpenter J., Preotiuc-Pietro D., Clark J., Flekova L., Smith L., Kern M. L.. (2018). The impact of actively open-minded thinking on social media communication. Judg. Decis. Mak. 13, 562 – 574. doi: doi:10.1017/S1930297500006598
  12. Celik S., Gracia P. (2026). Problematic social media use and adolescent mental well-being: a cross-national study on socioeconomic inequalities. Inf. Commun. Soc. 1–23. doi: doi:10.1080/1369118X.2026.2639568
  13. Cormier M., Murray N. E., Li S., Saad Hossne J., Marin-Dragu S., Muzamil M.. (2026). Screen usage and social media motives in gender and sexual minority adolescents. Arch. Sex. Behav. 55, 677 – 689. doi: doi:10.1007/s10508-025-03367-0
  14. DeWalt D. A., Thissen D., Stucky B. D., Langer M. M., Morgan DeWitt E., Irwin D. E.. (2013). PROMIS pediatric peer relationships scale: development of a peer relationships item bank as part of social health measurement. Health Psychol. 32, 1093 – 1103. doi: doi:10.1037/a0032670
  15. Dorčić T. M., Mohorić T., Kukić M. (2025). Why do adolescents use social media? validation of the croatian version of motives to use social networking sites scale. Eur. J. Mental Health 20, 1 – 13. doi: doi:10.5708/EJMH.20.2025.0046
  16. Dredge R., Gleeson J., Garcia X. (2015). The development and validation of the social networking experiences questionnaire: a measure of adolescent cyberbullying and its impact. Viol. Vict. 30, 798 – 812. doi: doi:10.1891/0886-6708.VV-D-14-00040
  17. Dunbar R. I. M., Frangou A., Grainger F., Pearce E. (2021). Laughter influences social bonding but not prosocial generosity to friends and strangers. PLoS ONE 16: e0256229. doi: doi:10.1371/journal.pone.0256229
  18. Ebesutani C., Regan J., Smith A., Reise S., Higa-McMillan C., Chorpita B. F. (2012). The 10-item positive and negative affect schedule for children, child and parent shortened versions: application of item response theory for more efficient assessment. J. Psychopathol. Behav. Assess. 34, 191 – 203. doi: doi:10.1007/s10862-011-9273-2
  19. Erikson E. (1968). Identity, Youth and Crisis. New York, NY: W. W. Norton and Company.
  20. Gingras M-P., Brendgen M., Beauchamp M. H., Séguin J. R., Tremblay R. E., Côté S. M.. (2025). Adolescents and social media: longitudinal links between motivations for using social media and subsequent internalizing symptoms. J. Youth Adolesc. 54, 807 – 820. doi: doi:10.1007/s10964-024-02097-1
  21. Gullone E., Taffe J. (2012). The emotion regulation questionnaire for children and adolescents (ERQ-CA): a psychometric evaluation. Psychol. Assess. 24, 409 – 417. doi: doi:10.1037/t07563-000
  22. Guttman H., Laporte L. (2002). Alexithymia, empathy, and psychological symptoms in a family context. Comprehens. Psych. 43, 448 – 455. doi: doi:10.1053/comp.2002.35905
  23. Holtgraves T. (2021). Understanding miscommunication: speech act recognition in digital contexts. Cogn. Sci. 45: e13023. doi: doi:10.1111/cogs.13023
  24. Holtgraves T. (2024). Emoji, speech acts, and perceived communicative success. J. Lang. Soc. Psychol. 43, 83 – 103. doi: doi:10.1177/0261927X231200450
  25. IBM SPSS Statistics (2022). IBM SPSS Statistics for Mac, Version 29.0. Armonk, NY: IBM Corp.
  26. Janssen L. H. C., Beyens I., Bij de Vaate N. A. J. D., van der Wal A., Valkenburg P. M., Keijsers L. (2025). Parent-adolescent communication in a digital world: A 100-day diary study. Child Dev. 96, 736 – 751. doi: doi:10.1111/cdev.14203
  27. Jarman H. K., Marques M. D., McLean S. A., Slater A., Paxton S. J. (2021). Motivations for social media use: associations with social media engagement and body satisfaction and well-being among adolescents. J. Youth Adolesc. 50, 2279 – 2293. doi: doi:10.1007/s10964-020-01390-z
  28. Ketibian C., Combes C., Potard C. (2026). Measures of social media use motives during adolescence: a systematic review. J. Media Psychol. Theor. Methods Appl. doi: doi:10.1027/1864-1105/a000506
  29. Kingsbury M., Coplan R. J. (2016). RU mad @ me? social anxiety and interpretation of ambiguous text messages. Comput. Hum. Behav. 54, 368 – 379. doi: doi:10.1016/j.chb.2015.08.032
  30. Kruger J., Epley N., Parker J., Ng Z.-W. (2005). Egocentrism over e-mail: Can we communicate as well as we think? J. Pers. Soc. Psychol. 89, 925 – 936. doi: doi:10.1037/0022-3514.89.6.925
  31. Kumar A., Epley N. (2021). It's surprisingly nice to hear you: Misunderstanding the impact of communication media can lead to suboptimal choices of how to connect with others. J. Exp. Psychol. Gener. 150, 595 – 607. doi: doi:10.1037/xge0000962
  32. Lapidot-Lefler N., Barak A. (2015). The benign online disinhibition effect: Could situational factors induce self-disclosure and prosocial behaviors? Cyberpsychol.: J. Psych. Res. Cyberspace 9: 3. doi: doi:10.5817/CP2015-2-3
  33. López-Sáez M. Á. (2025). A Practical Guide for The Inclusion of The Sexual and Gender Diversity Perspective in Research, 1st Edn. Madrid: Universidad Rey Juan Carlos y Ministerio de Igualdad.
  34. López-Sáez M. Á., Pérez-Torres V., Pastor Y., Lobato-Rincón L-L., Thomas H., Angulo-Brunet A. (2024). Social media uses amongst adolescents: Motives, minority stress and eudaimonic well-being. An. Psychol. 40, 272 – 279. doi: doi:10.6018/analesps.556871
  35. Maas C. J., Hox J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology 1, 86–92. doi. 10.1027/1614-2241.1.3.86
  36. Maksniemi E., Hietajärvi L., Ketonen E. E., Lonka K., Puukko K., Salmela-Aro K. (2022). Intraindividual associations between active social media use, exhaustion, and bedtime vary according to age—a longitudinal study across adolescence. J. Adolesc. 94, 401 – 414. doi: doi:10.1002/jad.12033
  37. Manago A. M., Walsh A. S., Barsigian L. L. (2023). The contributions of gender identification and gender ideologies to the purposes of social media use in adolescence. Front. Psychol. 13: 1011951. doi: doi:10.3389/fpsyg.2022.1011951
  38. Marino C., Gini G., Angelini F., Vieno A., Spada M. M. (2020). Social norms and e-motions in problematic social media use among adolescents. Addict. Behav. Rep. 11: 100250. doi: doi:10.1016/j.abrep.2020.100250
  39. Martinez-Pecino R., Garcia-Gavilán M. (2019). Likes and problematic Instagram use: The moderating role of self-esteem. Cyberpsychol. Behav. Soc. Network. 22, 412 – 416. doi: doi:10.1089/cyber.2018.0701
  40. Mayshak R., Sharman S. J., Zinkiewicz L., Hayley A. (2017). The influence of empathy and self-presentation on engagement with social networking website posts. Comput. Hum. Behav. 71, 362 – 377. doi: doi:10.1016/j.chb.2017.02.026
  41. McMahon T. P., Naragon-Gainey K. (2018). The moderating effect of maladaptive emotion regulation strategies on reappraisal: a daily diary study. Cogn. Ther. Res. 42, 552 – 564. doi: doi:10.1007/s10608-018-9913-x
  42. Mewhort-Buist T.A., Nilsen E.S., Bowman-Smith C.K. (2020). Children's communicative decisions are influenced by gender, shyness, and peer experiences. Merrill-Palmer Q. 66, 1 – 33. doi: doi:10.13110/merrpalmquar1982.66.1.0001
  43. Michikyan M., Suárez-Orozco C. (2016). Adolescent media and social media use: Implications for development. J. Adolesc. Res. 31, 411 – 414. doi: doi:10.1177/0743558416643801
  44. Miers A. C., Sumter S. R., Clark D. M., Leigh E. (2020). Interpretation bias in online and offline social environments and associations with social anxiety, peer victimization, and avoidance behavior. Cogn. Ther. Res. 44, 820 – 833. doi: doi:10.1007/s10608-020-10097-1
  45. Minich M., Moreno M. (2024). Real-world adolescent smartphone use is associated with improvements in mood: an ecological momentary assessment study. PLoS ONE 19: e0298422. doi: doi:10.1371/journal.pone.0298422
  46. Mishna F., Khoury-Kassabri M., Gadalla T., Daciuk J. (2012). Risk factors for involvement in cyber bullying: Victims, bullies and bully–victims. Child. Youth Serv. Rev. 34, 63 – 70. doi: doi:10.1016/j.childyouth.2011.08.032
  47. Moriguchi Y., Decety J., Ohnishi T., Maeda M., Mori T., Nemoto K.. (2007). Empathy and judging other's pain: an fMRI study of alexithymia. Cerebr. Cortex 17, 2223 – 2234. doi: doi:10.1093/cercor/bhl130
  48. Nesi J., Choukas-Bradley S., Prinstein M. J. (2018). Transformation of adolescent peer relations in the social media context: Part 1—A theoretical framework and application to dyadic peer relationships. Clin. Child Fam. Psychol. Rev. 21, 267 – 294. doi: doi:10.1007/s10567-018-0261-x
  49. Nezlek J. B. (2008). An introduction to multilevel modeling for social and personality psychology. Soc. Pers. Psychol. Comp. 2, 842 – 860. doi: doi:10.1111/j.1751-9004.2007.00059.x
  50. Ophir Y., Asterhan C. S., Schwarz B. B. (2019). The digital footprints of adolescent depression, social rejection and victimization of bullying on Facebook. Comput. Hum. Behav. 91, 62 – 71. doi: doi:10.1016/j.chb.2018.09.025
  51. Orben A. C., Dunbar R. I. M. (2017). Social media and relationship development: the effect of valence and intimacy of posts. Comput. Hum. Behav. 73, 489 – 498. doi: doi:10.1016/j.chb.2017.04.006
  52. Ouvrein G., De Backer C. J., Vandebosch H. (2018). Online celebrity aggression: a combination of low empathy and high moral disengagement? the relationship between empathy and moral disengagement and adolescents' online celebrity aggression. Comput. Hum. Behav. 89, 61 – 69. doi: doi:10.1016/j.chb.2018.07.029
  53. Overgaauw S., Rieffe C., Broekhof E., Crone E. A., Güroglu B. (2017). Assessing empathy across childhood and adolescence: validation of the empathy questionnaire for children and adolescents (EmQue-CA). Front. Psychol. 8: 870. doi: doi:10.3389/fpsyg.2017.00870
  54. Paccagnella O. (2011). Sample size and accuracy of estimates in multilevel models: new simulation results. Methodology 7, 111 – 120. doi: doi:10.1027/1614-2241/a000029
  55. Pennebaker J. W. Boyd, R. L., Booth R. J., Ashokkumar A., Francis M. E. (2022). Linguistic Inquiry and Word Count: LIWC-22. Pennebaker Conglomerates. Available online at: (Accessed December 10, 2023).
  56. Pew Research Center (2018). Teens' Social Media Habits and Experiences [Report]. Available online at: (Accessed November 28, 2018).
  57. Pew Research Center (2023). Teens and Social Media: Key findings From Pew Research Center Surveys [Report]. Available online at: (Accessed April 24, 2023).
  58. Pew Research Centre (2025). Teens, social media and AI chatbots 2025 [Report]. Washington, DC: Pew Research Center
  59. Pew Research Centre (2026). Teens' experiences on TikTok, Instagram, and Snapchat. [Report]. Washington, DC: Pew Research Center
  60. Politte-Corn M., Nick E. A., Kujawa A. (2023). Age-related differences in social media use, online social support, and depressive symptoms in adolescents and emerging adults. Child Adolesc. Mental Health 28, 497 – 503. doi: doi:10.1111/camh.12640
  61. Radovic A., Gmelin T., Stein B. D., Miller E. (2017). Depressed adolescents' positive and negative use of social media. J. Adolesc. 55, 5 – 15. doi: doi:10.1016/j.adolescence.2016.12.002
  62. Rimé B. (2007). “Interpersonal emotion regulation,” in Handbook of Emotion Regulation, eds. J. J. Gross (New York, NY: The Guilford Press), 466 – 485.
  63. Rimé B. (2009). Emotion elicits the social sharing of emotion: theory and empirical review. Emot. Rev. 1, 60 – 85. doi: doi:10.1177/1754073908097189
  64. Riordan M. A., Trichtinger L. A. (2017). Overconfidence at the keyboard: confidence and accuracy in interpreting affect in e-mail exchanges. Hum. Commun. Res. 43, 1 – 24. doi: doi:10.1111/hcre.12093
  65. Roghanizad M. M., Bohns V. K. (2017). Ask in person: you're less persuasive than you think over email. J. Exp. Soc. Psychol. 69, 223 – 226. doi: doi:10.1016/j.jesp.2016.10.002
  66. Salem A. A., Al-Huwailah A. H., Abdelsattar M., Al-Hamdan N., Derar E., Alazmi S.. (2023). Empathic skills training as a means of reducing cyberbullying among adolescents: an empirical evaluation. Int. J. Environ. Res. Public Health 20: 1846. doi: doi:10.3390/ijerph20031846
  67. Schreurs L., Lee A. Y., Liu X. “Sunny”, Hancock, J. T. (2024). When adolescents' self-worth depends on their social media feedback: a longitudinal investigation with depressive symptoms. Commun. Res. 51, 631 – 659. doi: doi:10.1177/00936502241233787
  68. Sherman L. E., Payton A. A., Hernandez L. M., Greenfield P. M., Dapretto M. (2016). The power of the like in adolescence: effects of peer influence on neural and behavioral responses to social media. Psychol. Sci. 27, 1027 – 1035. doi: doi:10.1177/0956797616645673
  69. Skogen J. C., Bøe T., Finserås T. R., Sivertsen B., Hella R. T. Hjetland, G. J. (2022). Lower subjective socioeconomic status is associated with increased risk of reporting negative experiences on social media. findings from the “LifeOnSoMe”-study. Front. Public Health 10: 873463. doi: doi:10.3389/fpubh.2022.873463
  70. Snijders T., Bosker R. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks, CA: Sage.
  71. Subrahmanyam K., Greenfield P. (2008). Online communication and adolescent relationships. Future Child. 18, 119 – 146. doi: doi:10.1353/foc.0.0006
  72. Suler J. (2004). The online disinhibition effect. CyberPsychol. Behav. 7, 321 – 326. doi: doi:10.1089/1094931041291295
  73. Svensson R., Johnson B., Olsson A. (2022). Does gender matter? the association between different digital media activities and adolescent well-being. BMC Public Health 22: 273. doi: doi:10.1186/s12889-022-12670-7
  74. Thompson R. A. (1994). Emotion regulation: a theme in search of definition. Monograph. Soc. Res. Child Dev. 59, 25 – 52. doi: doi:10.2307/1166137
  75. Tian L., Zhai R., Wang Y., Zhang H., Chen J. (2024). How different online self-presentations relate to different online feedback among adolescents: peer relationship as a mediator. Curr. Psychol. 43, 10693 – 10702. doi: doi:10.1007/s12144-023-05190-z
  76. Valkenburg P. (2022). Social media use and well-being: what we know and what we need to know. Curr. Opin. Psychol. 45: 101294. doi: doi:10.1016/j.copsyc.2021.12.006
  77. van der Wal A., Valkenburg P. M., van Driel I. I. (2024). In their own words: how adolescents use social media and how it affects them. Soc. Media Soc. 10, 1 – 11. doi: doi:10.1177/20563051241248591
  78. Verbeij T., Pouwels J. L., Beyens I., Valkenburg P. M. (2021). The accuracy and validity of self-reported social media use measures among adolescents. Comput. Hum. Behav. Rep. 3: 100090. doi: doi:10.1016/j.chbr.2021.100090
  79. Waterloo S. F., Baumgartner S. E., Peter J., Valkenburg P. M. (2018). Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. New Media Soc. 20, 1813 – 1831. doi: doi:10.1177/1461444817707349
  80. Weinstein E., James C. (2022). Behind Their Screens: What Teens are Facing (and Adults are Missing). (Cambridge, MA: MIT Press). doi: doi:10.7551/mitpress/14088.001.0001
  81. Wong S. L., McLellan R. (2024). Adolescents' motivation to use social network sites from a psychological needs perspective. J. Adolesc. 96, 305 – 321. doi: doi:10.1002/jad.12276
  82. Yang C.-C., Holden S. M., Ariati J. (2021). Social media and psychological well-being among youth: The multidimensional model of social media use. Clin. Child Fam. Psychol. Rev. 24, 631 – 650. doi: doi:10.1007/s10567-021-00359-z
  83. Zych I., Baldry A. C., Farrington D. P., Llorent V. J. (2019). Are children involved in cyberbullying low on empathy? a systematic review and meta-analysis of research on empathy versus different cyberbullying roles. Aggress. Violent Behav. 45, 83 – 97. doi: doi:10.1016/j.avb.2018.03.004

Citation

Bowman-Smith, C. K. & Nilsen, E. S. (2026). Wanting more: adolescents' expectations for social media responses from peers. Frontiers in Developmental Psychology, Volume 4. https://doi.org/
Received
Accepted
Published
Volume
4 - 2026

Copyright

Copyright © 2026 Bowman-Smith and Nilsen.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Elizabeth S. Nilsen

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

PerspectiveFriluftsliv (open-air life) as a cultural site of emotion socialization in early childhood in NorwayEvi Petersen, Natália Meireles Santos da Costa, Tuva Beyer BrochReviewScreen exposure and cognitive outcomes in school-aged children and adolescents: a narrative review through the lens of Cognitive Load TheoryHiya Al Kalbani, Erin Kitt-LewisBrief Research ReportShort-term familiarity shapes social preference but not word learning in preschoolersJina Ahn, Erica A. Cartmill, Catherine M. Sandhofer