AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment

Abstract-- 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. Depression affects over 280 million people globally, with severe outcomes, including approximately 700,000 suicides annually [1]. Despite available treatments, barriers such as stigma and limited access of healthcare treatments leave over 70% of affected individuals untreated [2]. The COVID-19 pandemic has intensified this crisis, highlighting the urgent need for effective and scalable methods for early symptom identification [3]. Social media platforms such as Facebook, X, and Reddit provide a rich source of user-generated content reflecting mental states, offering opportunities for automated depression assessment [4], [5]. Traditional interview-and questionnaire-based approaches, while informative, are resource-intensive and may lack scalability [6], [7].