Depression Risk Assessment in Social Media via Large Language Models
This work addresses the problem of scalable mental health monitoring for social media users, offering a cost-effective approach, but it is incremental as it applies existing LLM methods to a specific domain with competitive rather than groundbreaking results.
The paper tackled depression risk assessment in social media by proposing a system using Large Language Models for multi-label classification of depression-associated emotions and severity indexing, achieving competitive micro-F1 of 0.75 and macro-F1 of 0.70 on a dataset and analyzing over 469,000 Reddit comments to reveal stable risk profiles across communities.
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.