Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media
This work addresses early detection of depression for social media users, but it appears incremental as it builds on existing graph and prompt learning techniques.
The paper tackled the problem of early depression detection on social media by addressing issues of interpretability, data heterogeneity, and global user interactions, resulting in a method that significantly outperforms state-of-the-art approaches.
Massive social media data can reflect people's authentic thoughts, emotions, communication, etc., and therefore can be analyzed for early detection of mental health problems such as depression. Existing works about early depression detection on social media lacked interpretability and neglected the heterogeneity of social media data. Furthermore, they overlooked the global interaction among users. To address these issues, we develop a novel method that leverages a Heterogeneous Subgraph Network with Prompt Learning(HSNPL) and contrastive learning mechanisms. Specifically, prompt learning is employed to map users' implicit psychological symbols with excellent interpretability while deep semantic and diverse behavioral features are incorporated by a heterogeneous information network. Then, the heterogeneous graph network with a dual attention mechanism is constructed to model the relationships among heterogeneous social information at the feature level. Furthermore, the heterogeneous subgraph network integrating subgraph attention and self-supervised contrastive learning is developed to explore complicated interactions among users and groups at the user level. Extensive experimental results demonstrate that our proposed method significantly outperforms state-of-the-art methods for depression detection on social media.