The present study conceptualized and developed new measurement instruments to assess adolescents’ a) exposure to, b) liking, and c) posting of positive content on social media. By means of an integrative review of the literature, six focus groups and 14 in-depth interviews, 19 items were developed for each scale. Based on a cross-sectional study among 294 adolescents, EFA and CFA extracted two valid and reliable factors for exposure to positive social media content, one valid and reliable factor for liking positive social media content, and three valid and reliable factors for posting positive social media content. A short version of these three (multifactorial) scales was created and administered in a two-wave panel study among 1419 adolescents and in a cross-sectional study among 493 late adolescents. Test–retest reliability, structural validity, construct validity, and full metric invariance across age and gender were established for all short scales, except for the posting scale for which only partial metric invariance was achieved across gender.

Due to technological advancements such as generative artificial intelligence (AI) and large language models, chatbots enable increasingly human-like, real-time conversations through text (e.g., OpenAI’s ChatGPT) and voice (e.g., Amazon’s Alexa). One AI chatbot that is specifically designed to meet the social-supportive needs of youth is Snapchat’s My AI. Given its increasing popularity among adolescents, the present study investigated whether adolescents’ likelihood of using My AI, as well as their positive or negative emotional experiences from interacting with the chatbot, is related to socio-demographic factors (i.e., gender, age, and socioeconomic status (SES)). A cross-sectional study was conducted among 303 adolescents (64.1% girls, 35.9% boys, 1.0% other, 0.7% preferred not to say their gender; M_age = 15.89, SD_age = 1.69). The findings revealed that younger adolescents were more likely to use My AI and experienced more positive emotions from these interactions than older adolescents. No significant relationships were found for gender or SES. These results highlight the potential for age to play a critical role in shaping adolescents’ engagement with AI chatbots on social media and their emotional outcomes from such interactions, underscoring the need to consider developmental factors in AI design and policy.

While social media is assumed to exacerbate adolescents’ depressive symptoms, research findings are ambiguous. One way to move the field forward is by looking beyond time spent on social media and considering subjective experiences. The current three-wave longitudinal panel study examines the within- and between-person relations between adolescents’ self-worth dependency on social media feedback and depressive symptoms. About 1,607 adolescents participated in two of the three waves, yet a third had to be excluded due to failing an attention check. Among the analytical sample of 1,032 adolescents, we found that adolescents who derived more of their self-worth from social media feedback were also more depressed, as indicated by a positive correlation at the between-person level. No support was found for within-person associations over time. These results highlight the need to examine effects of subjective experiences with social media by separating within- and between-person dynamics to reach more precise conclusions.