Depression Risk Assessment in Social Media via Large Language Models

• URL: http://arxiv.org/abs/2604.19887v1
• Date: Tue, 21 Apr 2026 18:09:37 GMT
• Title: Depression Risk Assessment in Social Media via Large Language Models
• Abstract summary: Depression is one of the most prevalent and debilitating mental health conditions worldwide.<n>Social media platforms provide a rich source of naturalistic linguistic signals for the automated monitoring of psychological well-being.<n>We propose a system based on Large Language Models (LLMs) for depression risk assessment in Reddit posts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic linguistic signals for the automated monitoring of psychological well-being. In this work, we propose a system based on Large Language Models (LLMs) for depression risk assessment in Reddit posts, through multi-label classification of eight depression-associated emotions and the computation of a weighted severity index. The method is evaluated in a zero-shot setting on the annotated DepressionEmo dataset (~6,000 posts) and applied in-the-wild to 469,692 comments collected from four subreddits over the period 2024-2025. Our best model, gemma3:27b, achieves micro-F1 = 0.75 and macro-F1 = 0.70, results competitive with purpose-built fine-tuned models (BART: micro-F1 = 0.80, macro-F1 = 0.76). The in-the-wild analysis reveals consistent and temporally stable risk profiles across communities, with marked differences between r/depression and r/anxiety. Our findings demonstrate the feasibility of a cost-effective, scalable approach for large-scale psychological monitoring.

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