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Article http://dx.doi.org/10.26855/jhass.2025.06.024

Understanding Adolescent Social Media Use: A Computational Modeling Perspective

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Zimo Zhang^1,*, Shen Tian^2,*

¹Department of Psychology, University of California, Irvine, Irvine, CA 92697, USA.

²Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 171 77, Sweden.

*Corresponding author: Zimo Zhang, Shen Tian

Published: July 9,2025

Abstract

Social media has become deeply integrated into adolescents’ everyday lives, but traditional research methods, such as surveys, sometimes fall short of capturing the rapidly evolving psychological mechanisms behind digital engagement. This paper reviews how computational modeling, especially reinforcement learning (RL), provides a more precise and real-time approach to studying adolescent behavior on social platforms. By modeling feedback sensitivity and reward-driven behavior of adolescents, RL frameworks can explain the development of compulsive usage patterns, particularly during adolescence when the brain is highly responsive to peer evaluation and emotional feedback. Empirical studies and neuroimaging findings show that adolescents exhibit heightened learning rates and greater mood fluctuations in response to social feedback than adults. Computational approaches also offer practical pathways for improving platform design and informing digital interventions. This paper advocates for integrating RL models into psychological research to better understand individual variability and develop data-driven strategies for promoting healthier digital habits among youth.

Keywords

Adolescents; reinforcement learning; social media; computational modeling; feedback sensitivity

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How to cite this paper

Understanding Adolescent Social Media Use: A Computational Modeling Perspective

How to cite this paper: Zimo Zhang, Shen Tian. (2025) Understanding Adolescent Social Media Use: A Computational Modeling Perspective. Journal of Humanities, Arts and Social Science, 9(6), 1191-1195.

DOI: http://dx.doi.org/10.26855/jhass.2025.06.024