Enhancing Depression Detection on Social Media with DSM-V Guided RAG and Fine-Tuned DeepSeek-R

Depression significantly contributes to global disability, yet remains underdiagnosed due to lack of awareness. Social media platforms provide rich, real-time data reflecting users' emotional and psychological states, making them promising resources for automated depression detection. However, general-purpose Large Language Models (LLMs) often perform suboptimally in clinical domains, lacking interpretability and domain specificity. To address these limitations, this study introduces a knowledge-augmented framework combining Retrieval-Augmented Generation (RAG) guided by clinical psychiatric criteria from the DSM-V with a fine-tuned DeepSeek-R1-7B model. We constructed a domain specific knowledge base comprising 329 expert-reviewed, retrieval-friendly DSM-V-derived entries. By retrieving relevant DSM-V-TR diagnostic criteria alongside user posts, our framework provides clinically grounded contextual information that may support human review by relating model predictions to established psychiatric criteria. Experiments on three balanced social media datasets showed that the DSM-V-guided RAG module, when combined with fine-tuned DeepSeek-R1-7B, achieved improved performance over the compared settings under the current experimental protocol. The retrieved DSM-V entries also provided clinically grounded contextual information that may help interpret model predictions. These findings suggest that DSM-V-guided knowledge augmentation is a promising direction for improving the transparency of benchmark-based depression detection on social media.