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

Hamad Zogan1,2, Imran Razzak3, Xianzhi Wang1

  • 1University of Technology Sydney (UTS), Sydney, Australia.

World Wide Web
|February 2, 2022
PubMed
Summary

We developed an explainable AI model for detecting depression on social media. Our method, MDHAN, improves prediction accuracy and provides clear explanations for its results.

Keywords:
Deep learningDepression detectionExplainabilityMachine learningSocial network

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Area of Science:

  • Artificial Intelligence
  • Computational Social Science
  • Psychology

Background:

  • Model explainability is crucial for trust, especially in medical applications.
  • Existing machine learning models often lack transparency in predictions.
  • Automatic depression prediction using machine learning is challenging due to model obscurity.

Purpose of the Study:

  • To propose an explainable AI model for automatic depression detection on social media.
  • To enhance user trust by providing insights into model predictions.
  • To improve the accuracy of depression detection in social media users.

Main Methods:

  • Developed MDHAN (Multi-Aspect Depression Detection with Hierarchical Attention Network).
  • Utilized a hierarchical attention mechanism at tweet and word levels.
  • Incorporated multi-aspect features from user posts and Twitter data.

Main Results:

  • MDHAN significantly outperformed existing baseline methods.
  • The model demonstrated effectiveness in combining deep learning with multi-aspect features.
  • MDHAN improved predictive performance for social media depression detection.

Conclusions:

  • MDHAN offers an effective and explainable approach to detecting depression on social media.
  • The model provides adequate evidence to support its predictions.
  • Explainable AI is vital for reliable mental health monitoring tools.