Accepted for/Published in: JMIR Mental Health
Date Submitted: Jan 18, 2021
Date Accepted: Dec 16, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Detecting and Measuring Depression on Social Media by Machine Learning Approach: a Review
ABSTRACT
Background:
Detection of depression gained prominence soon after this troublesome disease emerged as a serious public health concern worldwide.
Objective:
The aim of this review intends to summarize methods to measure depressive symptoms on social media by machine learning approaches.
Methods:
A bibliographic retrieval was conducted from January 1990 until December 2020 in Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed. Seventeen studies met the inclusion criteria.
Results:
Of the fifteen studies, ten defined depression based on self-reported mental status, five defined based on self-declared mental status, and the last two were based on community membership. Besides, among the fifteen studies, thirteen conducted depression detection with Supervised Learning (SL) approaches, three used Unsupervised Learning (UL) approaches to detect depression, while the remaining one did not report the ML approach. Challenges such as sample scale, optimizing of predicting approaches and features, generalizability, issues about privacy, and ethic are still open to research.
Conclusions:
ML approaches might work effectively for depression detection using text data from users on social media and it could serve as a complementary tool in practice about public psychological health.
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Copyright
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