Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

Hamad ZOGAN, Imran RAZZAK, Xianzhi WANG, Shoaib JAMEEL, Guandong XU

Research output: Contribution to journalArticlespeer-review

42 Citations (Scopus)

Abstract

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction. Copyright © 2022 The Author(s).

Original languageEnglish
Pages (from-to)281-304
JournalWorld Wide Web
Volume25
DOIs
Publication statusPublished - Jan 2022

Citation

Zogan, H., Razzak, I., Wang, X., Jameel, S., & Xu, G. (2022). Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web, 25, 281-304. https://doi.org/10.1007/s11280-021-00992-2

Keywords

  • Depression detection
  • Social network
  • Deep learning
  • Machine learning
  • Explainability

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