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Detecting depression tendency with multimodal features.

Hui Zhang1, Hong Wang1, Shu Han1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.

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Summary
This summary is machine-generated.

This study introduces the Multimodal Transformer Depression Detection (MTDD) model, a novel neural network approach for identifying early signs of depression in social media users. The MTDD model achieves state-of-the-art results, demonstrating high accuracy in detecting depressive tendencies.

Keywords:
Deep learningDepressive tendency detectionMulti-feature fusionSocial media

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Area of Science:

  • Computational social science
  • Artificial intelligence in mental health
  • Natural language processing for mental health analysis

Background:

  • Depression significantly impacts individual well-being and societal health, necessitating early detection and intervention.
  • Social media platforms offer a rich source of data for identifying depressive tendencies due to users expressing emotions and seeking support online.
  • Existing methods for depression detection often rely on subjective data or single models, limiting their accuracy and generalizability.

Purpose of the Study:

  • To develop and validate a novel hybrid neural network model, MTDD, for detecting depressive tendencies in social media users.
  • To leverage objective, low-cost social media data for depression detection, overcoming limitations of subjective expert consultations and incomplete datasets.
  • To enhance the robustness and generalization ability of depression detection models by integrating multimodal features.

Main Methods:

  • Proposed a hybrid deep neural network model, MTDD, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks.
  • Utilized multimodal features, including text, semantic, and domain knowledge, for vector representation of depression-prone text.
  • Developed a post-level detection method analyzing user content on social platforms.

Main Results:

  • The MTDD model achieved a 95% F1 score in detecting users with depressive tendencies.
  • The model demonstrated state-of-the-art (SOTA) performance compared to existing methods.
  • Extensive experiments confirmed the model's effectiveness and superiority.

Conclusions:

  • The MTDD model offers a more effective approach to detecting depressive users on social media, facilitating early diagnosis and treatment.
  • The model's performance surpasses that of many recent depression tendency detection models.
  • This research highlights the potential of social media data and advanced AI for mental health monitoring.