LLM-Based Depression Risk Assessment in Reddit Posts
A novel approach employs Large Language Models (LLMs) to evaluate the risk of depression in Reddit comments by identifying eight emotions linked to depression and calculating a severity index with weighted scores. This technique was tested in a zero-shot manner on the DepressionEmo dataset, which contains approximately 6,000 posts, and was also utilized on 469,692 comments from four subreddits during 2024-2025. The top-performing model, gemma3:27b, recorded a micro-F1 score of 0.75 and a macro-F1 score of 0.70, showing competitiveness with the fine-tuned BART model (micro-F1=0.80, macro-F1=0.76). This research underscores the potential of LLMs for scalable mental health surveillance through social media platforms.
Key facts
- System uses LLMs for multi-label classification of eight depression-associated emotions.
- Evaluated on DepressionEmo dataset with ~6,000 posts.
- Applied in-the-wild to 469,692 comments from four subreddits.
- Data collected over the period 2024-2025.
- Best model: gemma3:27b.
- Achieved micro-F1=0.75, macro-F1=0.70.
- Fine-tuned BART achieved micro-F1=0.80, macro-F1=0.76.
- Published on arXiv with ID 2604.19887.
Entities
Institutions
- arXiv