AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment
This work addresses the problem of explainable mental health assessment from social media for clinicians or researchers, with incremental improvements in performance and interpretability.
The paper tackled depression severity assessment from social media posts by proposing AttentionDep, a domain-aware attention model that fuses contextual and domain knowledge, resulting in over 5% improvement in graded F1 score compared to state-of-the-art baselines.
In today's interconnected society, social media platforms provide a window into individuals' thoughts, emotions, and mental states. This paper explores the use of platforms like Facebook, X (formerly Twitter), and Reddit for depression severity detection. We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation by fusing contextual and domain knowledge. Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms highlighting clinically relevant tokens. Domain knowledge from a curated mental health knowledge graph is incorporated through a cross-attention mechanism, enriching the contextual features. Finally, depression severity is predicted using an ordinal regression framework that respects the clinical-relevance and natural ordering of severity levels. Our experiments demonstrate that AttentionDep outperforms state-of-the-art baselines by over 5% in graded F1 score across datasets, while providing interpretable insights into its predictions. This work advances the development of trustworthy and transparent AI systems for mental health assessment from social media.