Understanding Individual Differences in Adolescents’ Emotional Responses to Social Media Through Human-Centered Causal and Dynamic Modeling

Abstract

Social media platforms shape adolescents’ daily social experiences by making peer feedback, visibility and social comparison highly salient. Yet research on social media and adolescent well-being remains inconsistent, partly because many studies rely on broad between-person measures that obscure within-person dynamics and developmental differences. This paper proposes a human-centered computational framework using Ecological Momentary Assessment (EMA) to examine how adolescents’ emotional responses to online validation vary within individuals and across developmental stages. Focusing on early adolescents (ages 13–15) and later adolescents (ages 16–20), the framework analyzes longitudinal EMA data on online validation events, emotional state and contextual factors using a three-part modeling pipeline: (1) multilevel within-person models to estimate immediate changes relative to individual baselines, (2) Dynamic Structural Equation Modeling (DSEM) to capture temporal dependencies and carryover effects across prompts and (3) causal forest modeling to estimate heterogeneous effects and identify profiles of sensitivity to validation. As an initial methodological step, the framework is validated using a synthetic dataset designed to reflect realistic EMA-style patterns of behavior, missingness and emotional dynamics before real-world deployment. The proposed pipeline provides a reusable and developmentally sensitive approach for studying adolescent digital well-being with greater precision than coarse between-person measures of social media use.

Keywords: Adolescents, Online Validation, Social Media, Ecological Momentary Assessment, Intensive Longitudinal Modeling, Dynamic Structural Equation Modeling, Causal Forest, Digital Well-being

DOI: 10.54941/ahfe1007323