AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment

• URL: http://arxiv.org/abs/2510.00706v1
• Date: Wed, 01 Oct 2025 09:20:53 GMT
• Title: AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment
• Authors: Yusif Ibrahimov, Tarique Anwar, Tommy Yuan, Turan Mutallimov, Elgun Hasanov,
• Abstract summary: We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation.<n>Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms highlighting clinically relevant tokens.<n>Experiments demonstrate that AttentionDep outperforms state-of-the-art baselines by over 5% in graded F1 score across datasets.
• Score: 0.16777183511743465
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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