SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in detecting depression on Twitter

Heng Ee Tay, Mei Kuan Lim, Chun Yong Chong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Conventional approaches to identify depression are not scalable and the public has limited awareness of mental health, especially in developing countries. As evident by recent studies, social media has the potential to complement mental health screening on a greater scale. The vast amount of first-person narrative posts in chronological order can provide insights into one’s thoughts, feelings, behavior, or mood for some time, enabling a better understanding of depression symptoms reflected in the online space. In this paper, we propose SERCNN, which improves the user representation by (1) stacking two pretrained embeddings from different domains and (2) reintroducing the embedding context to the MLP classifier. Our SERCNN shows great performance over state-of-the-art and other baselines, achieving 93.7% accuracy in a 5-fold cross-validation setting. Since not all users share the same level of online activity, we introduced the concept of a fixed observation window that quantifies the observation period in a predefined number of posts. With as minimal as 10 posts per user, SERCNN performed exceptionally well with an 87% accuracy, which is on par with the BERT model, while having 98% less in the number of parameters. Our findings open up a promising direction for detecting depression on social media with a smaller number of posts for inference, toward creating solutions for a cost-effective and timely intervention. We hope that our work can bring this research area closer to real-world adoption in existing clinical practice.

Original languageEnglish
Title of host publicationICPR 2022 International Workshops and Challenges, Montreal, QC, Canada, August 21–25, 2022 Proceedings, Part I
EditorsJean-Jacques Rousseau, Bill Kapralos
Place of PublicationCham Switzerland
PublisherSpringer
Pages617-631
Number of pages15
ISBN (Electronic)9783031376603
ISBN (Print)9783031376597
DOIs
Publication statusPublished - 2023
EventInternational Conference on Pattern Recognition 2022 - Montreal, Canada
Duration: 21 Aug 202225 Aug 2022
Conference number: 26th
https://ieeexplore.ieee.org/xpl/conhome/9956007/proceeding (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13643
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Pattern Recognition 2022
Abbreviated titleICPR 2022
Country/TerritoryCanada
CityMontreal
Period21/08/2225/08/22
Internet address

Keywords

  • Deep learning
  • Depression detection
  • Social media

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