# Emotions, Context, and Substance Use in Adolescents: A Large Language Model Analysis of Reddit Posts

arXiv: https://arxiv.org/abs/2501.14037 · HTML: https://mlsanity.com/paper/2501.14037/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing](https://mlsanity.com/paper/2407.08095/) (2024)
- [Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways](https://mlsanity.com/paper/2406.11980/) (2024)
- [AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models](https://mlsanity.com/paper/2409.18339/) (2024)
- [Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines](https://mlsanity.com/paper/1510.01006/) (2015)
- [Academic Torrents: Scalable Data Distribution](https://mlsanity.com/paper/1603.04395/) (2016)

## Built on by (most cited first, 2 of 2)

- [Can LLMs Infer Personality from Real World Conversations?](https://mlsanity.com/paper/2507.14355/) (2025)
- [AI-Powered Early Diagnosis of Mental Health Disorders from Real-World Clinical Conversations](https://mlsanity.com/paper/2510.14937/) (2025)

## Abstract (arXiv)

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 posts, while joy dominated non-substance discussions. Guilt and shame diverged in function: guilt often reflected regret and self-reflection, whereas shame reinforced risky behaviors through peer performance. Peer influence emerged as the strongest contextual factor, closely tied to sadness, fear, and guilt. Family and school environments acted as both risk and protective factors depending on relational quality and stress levels. Overall, adolescent substance-use discussions reflected a dynamic interplay of emotion, social context, and coping behavior. By integrating statistical analysis, interpretable models, and LLM-based thematic exploration, this study demonstrates the value of mixed computational approaches for uncovering the emotional and contextual mechanisms underlying adolescent risk behavior.