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.

翻译：在当今互联社会中，社交媒体平台为洞察个体的思想、情感与心理状态提供了窗口。本文探索利用Facebook、X（原Twitter）和Reddit等平台进行抑郁症严重程度检测。我们提出AttentionDep模型，这是一种领域感知注意力模型，通过融合上下文信息与领域知识驱动可解释的抑郁症严重程度评估。该模型采用单字词与双字词对帖子进行分层编码，并利用注意力机制突出临床相关词汇标记。通过跨注意力机制整合来自精心构建的心理健康知识图谱的领域知识，从而丰富上下文特征。最后，采用尊重临床相关性及严重程度自然顺序的序数回归框架预测抑郁症严重程度。实验表明，AttentionDep在多个数据集上的分级F1分数超越现有最优基线模型超过5%，同时为其预测提供可解释的洞察。此项工作推动了基于社交媒体的心理健康评估领域可信赖、透明人工智能系统的发展。