Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media

TL;DR

This paper introduces a novel interpretable method using heterogeneous subgraph networks and prompt learning to improve early depression detection on social media, addressing interpretability, data heterogeneity, and global user interactions.

Contribution

It proposes a Heterogeneous Subgraph Network with Prompt Learning (HSNPL) that combines interpretability, heterogeneous data modeling, and contrastive learning for depression detection.

Findings

Significantly outperforms state-of-the-art methods

Effectively models complex user interactions

Enhances interpretability of depression detection

Abstract

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.