DepressionNet: A novel summarization boosted deep framework for depression detection on social media

Hamad ZOGAN, Imran RAZZAK, Shoaib JAMEEL, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

43 Citations (Scopus)

Abstract

Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns including user's social interactions. The downside is that these models are trained on several irrelevant content which might not be crucial towards detecting a depressed user. Besides, these content have a negative impact on the overall efficiency and effectiveness of the model. To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content. The content then goes to our novel deep learning framework comprising of a unified learning machinery comprising of Convolutional Neural Network (CNN) coupled with attention-enhanced Gated Recurrent Units (GRU) models leading to better empirical performance than existing strong baselines. Copyright © 2021 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 44th International ACM SIGIR Conference on research and development in information retrieval
Place of PublicationNew York
PublisherThe Association for Computing Machinery
Pages133-142
ISBN (Electronic)9781450380379
DOIs
Publication statusPublished - Jul 2021

Citation

Zogan, H., Razzak, I., Jameel, S., & Xu, G. (2021). DepressionNet: A novel summarization boosted deep framework for depression detection on social media. In Proceedings of the 44th International ACM SIGIR Conference on research and development in information retrieval (pp. 133-142). The Association for Computing Machinery. https://doi.org/10.1145/3404835.3462938

Keywords

  • Depression detection
  • Social network
  • Deep learning
  • Machine learning
  • Text summarization

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