Leveraging Large Language Models to Analyze Emotional and Contextual Drivers of Teen Substance Use in Online Discussions

Introduction

Adolescent substance use (SU) remains a critical public health challenge. Traditional surveillance methods, such as annual surveys or clinical assessments, often fail to capture the real-time emotional and social triggers—the "microprocesses"—that lead to substance use in daily life. This study addresses this gap by analyzing nearly 40,000 posts from Reddit’s r/teenagers community (2018–2022). By leveraging Large Language Models (LLMs) for high-fidelity annotation, the researchers move beyond simple sentiment analysis to uncover the nuanced emotional and contextual drivers behind adolescent SU discussions.

Methods

The study employed a multi-stage analytic pipeline to process and analyze the large-scale social media dataset, bridging the gap between big data and deep psychological insight.

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1. Data Acquisition & Filtering: 38,950 posts were scraped and filtered using a curated keyword list to identify SU-related content versus control posts.
2. LLM-Based Annotation: GPT-4 was utilized to label posts for seven discrete emotions (joy, guilt, anger, disgust, fear, sadness, shame) and three social contexts (family, peers, school).
3. Predictive Modeling: An XGBoost classifier was trained to distinguish SU from non-SU posts based on these emotional and contextual features.
4. Interpretability: SHAP (Shapley Additive Explanations) values were calculated to identify the most influential predictors.
5. Thematic Extraction: GPT-4 performed inductive thematic analysis to summarize qualitative "storylines" from the data.

Key Results

The analysis revealed distinct emotional profiles for substance-related discussions. Negative emotions—specifically sadness, guilt, fear, and disgust—were significantly more prevalent in SU posts compared to the control group.

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• Emotion Distribution: Joy was the strongest indicator of non-SU posts (22% vs. 15%), while sadness and fear showed marked increases in SU-related content. Statistical tests confirmed these differences were highly significant (p < .001).
• Peer Influence: This was the most significant contextual driver, appearing in 72% of SU posts compared to only 41% of control posts. SHAP analysis confirmed peer influence as the single most important feature for the model’s predictions.
• Model Performance: The XGBoost model achieved an accuracy of 0.67 (F1 = 0.71), demonstrating that psychological "signals" alone are robust enough to identify SU content without relying on specific drug-related keywords.

Discussion and Insights

A striking finding was the functional divergence between guilt and shame. While guilt in SU posts often signaled regret and a desire for reparation, shame was frequently tied to "identity performance" and social validation, which may reinforce risky behaviors. Additionally, temporal analysis indicated that SU discussions are not uniform; they peak during school breaks and holidays, suggesting that unstructured time and increased social activity represent significant risk windows for adolescents.

Limitations and Future Work

The study is limited by the anonymity of Reddit, which prevents verification of user demographics. Additionally, the results are dependent on the specific biases and prompt sensitivities of the LLM used. Future research should explore longitudinal data to track emotional trajectories over time and expand analysis to other platforms like TikTok or Instagram to provide a more representative view of the modern adolescent experience.

@misc{zhu2025leveraging,
  author = {Jianfeng Zhu and Ruoming Jin and Hailong Jiang and Yulan Wang and Xinyu Zhang and Karin G. Coifman},
  title = {{Leveraging Large Language Models to Analyze Emotional and Contextual Drivers of Teen Substance Use in Online Discussions}},
  year = {2025},
  eprint = {2501.14037},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2501.14037},
}