Emotions, Context, and Substance Use in Adolescents: A Large Language Model Analysis of Reddit Posts
Jianfeng Zhu, Hailong Jiang, Yulan Wang, Karin G. Coifman, Ruoming Jin, Deric R. Kenne

TL;DR
This study uses large language models to analyze Reddit posts from teenagers, revealing how emotions and social contexts influence adolescent substance use discussions and behaviors.
Contribution
It introduces a novel mixed computational approach combining statistical, interpretable machine learning, and thematic analysis to understand emotional and contextual factors in adolescent substance use.
Findings
Negative emotions like sadness and guilt are more common in substance-use posts.
Peer influence is the strongest contextual factor linked to substance discussions.
Family and school environments can act as risk or protective factors depending on context.
Abstract
Early substance use during adolescence increases the risk of later substance use disorders and mental health problems, yet the emotional and contextual factors driving these behaviors remain poorly understood. This study analyzed 23000 substance-use related posts and an equal number of non-substance posts from Reddit's r/teenagers community (2018-2022). Posts were annotated for six discrete emotions (sadness, anger, joy, guilt, fear, disgust) and contextual factors (family, peers, school) using large language models (LLMs). Statistical analyses compared group differences, and interpretable machine learning (SHAP) identified key predictors of substance-use discussions. LLM-assisted thematic coding further revealed latent psychosocial themes linking emotions with contexts. Negative emotions, especially sadness, guilt, fear, and disgust, were significantly more common in substance-use…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Online Learning and Analytics · Mental Health via Writing
MethodsHeatmap
