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This paper explores the use of Large Language Models (LLMs) for automated depression risk assessment using Reddit posts. The approach involves multi-label classification of eight depression-associated emotions, followed by computation of a weighted severity index to quantify depression risk. Evaluated in a zero-shot setting on the DepressionEmo dataset, the gemma3:27b model achieved competitive performance (micro-F1 = 0.75, macro-F1 = 0.70) compared to fine-tuned models, demonstrating the potential for scalable psychological monitoring.
Zero-shot LLMs can achieve surprisingly competitive performance in depression risk assessment from social media, rivaling fine-tuned models without the data-hungry training.
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.