Automatic Depressive Symptom Detection on Social Media Using the BDI-II

Automatic depression detection from social media has been widely explored as a complementary approach to mental health assessment; however, most existing work has focused on binary user-level classification, paying limited attention to how individual depressive symptoms are linguistically manifested in online discourse. This study addresses this limitation through symptom-level depression detection grounded in the 21 items of the BDI-II. The evaluation considers general-purpose transformer models, architectures adapted to the mental health domain, and approaches based on embeddings leveraging large language models across multiple editions of the eRisk benchmark. Results indicate that embedding-based approaches such as GPT-4+SVM and LLaMA+SVM, provide competitive performance with lower computational cost, while domain-adapted models consistently outperform general-purpose transformers. Symptom-level analysis reveals that affective symptoms are more reliably detected, whereas cognitively complex, behavioral, or sensitive symptoms remain underrepresented in social media text.

Palabras clave: Depression, Beck Depression Inventory, Social Media