Detect Depression from Social Networks with Sentiment Knowledge Sharing

TL;DR

This paper introduces DeSK, a multi-task learning framework that leverages shared sentiment knowledge to improve depression detection from social network messages across multiple languages.

Contribution

It proposes a novel multi-task training framework that incorporates sentiment knowledge sharing to enhance depression detection accuracy.

Findings

DeSK outperforms baseline models on Chinese and English datasets.

Sharing sentiment knowledge improves depression detection effectiveness.

The approach demonstrates cross-lingual robustness.

Abstract

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.