Semantic distillation and enhanced diagnostic alignment: A novel approach for depression detection in social media

Abstract

The growing prevalence of depression underscores the need for accessible detection methods. Social media provides an invaluable platform for identifying signs of depression, overcoming the limitations of traditional assessments. However, this task is fraught with challenges such as noisy data, insufficient labeled datasets, and limited integration of domain-specific knowledge, which hinder the effectiveness of existing models. To address these issues, we propose a novel framework consisting of three key components. First, the Unsupervised Multi-Layer Information Distillation Module employs unsupervised learning techniques to extract meaningful posts from noisy social media data. Second, the Domain Knowledge Enhancement Module with a Memory Update Mechanism addresses the challenge of sparse labeled data by incorporating continuous learning and integrating domain-specific knowledge. Finally, the mIRT-based User Depression Diagnosis Module utilizes multidimensional item response theory (mIRT) to assess symptom severity across multiple dimensions, enhancing the interpretability of the depression diagnosis. Experiments conducted on two real-world datasets demonstrate the effectiveness of our model, achieving accuracies of 97.45% and 94.54%. This framework improves both the accuracy and interpretability of social media-based depression detection, offering a promising solution for mental health monitoring.