Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings

תקציר מקורי באנגליתarXiv:2506.06616v2 Announce Type: replace Abstract: Accurate and interpretable detection of depressive language in social media can support early identification of mental health conditions and inform timely interventions. In this paper, we investigate the use of large language models (LLMs) and traditional machine learning classifiers for three social media-based mental health prediction tasks: binary depression classification, depression severity classification, and differential diagnosis among depression, PTSD, and anxiety. We compare zero-shot LLMs with supervised classifiers trained on conventional text embeddings, psycholinguistic features, and embeddings derived from LLM-generated mental health summaries. Across multiple publicly available social media text datasets and five-fold cro