This paper is in the following e-collection/theme issue:

Reviews in Digital Mental Health (264) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1800) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1836) Peer-to-Peer Support and Online Communities (725) Depression and Mood Disorders; Suicide Prevention (1532) Machine Learning (1769) Artificial Intelligence (1773) Mass Media/Social Media Communication and Campaigns (517)

Published on in Vol 9, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27244, first published .
Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Authors of this article:

Danxia Liu1 Author Orcid Image ;   Xing Lin Feng2 Author Orcid Image ;   Farooq Ahmed3, 4 Author Orcid Image ;   Muhammad Shahid5 Author Orcid Image ;   Jing Guo2 Author Orcid Image
Article Authors Cited by (58) Tweetations (4) Metrics

Journals

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Conference Proceedings

  1. Zhong M, Van Zoest V, Bilal A, Papadopoulos F, Castellano G. Proceedings of the 2022 International Conference on Multimodal Interaction. Unimodal vs. Multimodal Prediction of Antenatal Depression from Smartphone-based Survey Data in a Longitudinal Study View
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  6. Lorenzoni G, Velmovitsky P, Alencar P, Cowan D. 2024 IEEE International Conference on Big Data (BigData). GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study View
  7. Kavitha G, Veena K. 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). Research on User Behaviour Prediction in Social Networks Based on Deep Learning Model View
  8. Dileep A, Zakkir T, Jayamohan S. THE 6TH INTERNATIONAL CONFERENCE OF ICE-ELINVO 2023: Digital Solutions for Sustainable and Green Development. Predicting depression levels on social media engagement: A machine learning approach View
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