Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review Article Swipe
Danxia Liu
,
Xing Lin Feng
,
Farooq Ahmed
,
Muhammad Shahid
,
Jing Guo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2196/27244
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2196/27244
Background Detection of depression gained prominence soon after this troublesome disease emerged as a serious public health concern worldwide. Objective This systematic review aims to summarize the findings of previous studies concerning applying machine learning (ML) methods to text data from social media to detect depressive symptoms and to suggest directions for future research in this area. Methods A bibliographic search was conducted for the period of January 1990 to December 2020 in Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed. Two reviewers retrieved and independently assessed the 418 studies consisting of 322 articles identified through database searching and 96 articles identified through other sources; 17 of the studies met the criteria for inclusion. Results Of the 17 studies, 10 had identified depression based on researcher-inferred mental status, 5 had identified it based on users’ own descriptions of their mental status, and 2 were identified based on community membership. The ML approaches of 13 of the 17 studies were supervised learning approaches, while 3 used unsupervised learning approaches; the remaining 1 study did not describe its ML approach. Challenges in areas such as sampling, optimization of approaches to prediction and their features, generalizability, privacy, and other ethical issues call for further research. Conclusions ML approaches applied to text data from users on social media can work effectively in depression detection and could serve as complementary tools in public mental health practice.
Related Topics
Concepts
PsycINFO
Generalizability theory
Mental health
MEDLINE
Social media
Depression (economics)
Psychology
Inclusion (mineral)
Public health
Computer science
Artificial intelligence
Machine learning
Data science
Medicine
Psychiatry
World Wide Web
Social psychology
Developmental psychology
Political science
Law
Macroeconomics
Economics
Nursing
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.2196/27244
- https://mental.jmir.org/2022/3/e27244/PDF
- OA Status
- gold
- Cited By
- 91
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205555191
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4205555191Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/27244Digital Object Identifier
- Title
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Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic ReviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-16Full publication date if available
- Authors
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Danxia Liu, Xing Lin Feng, Farooq Ahmed, Muhammad Shahid, Jing GuoList of authors in order
- Landing page
-
https://doi.org/10.2196/27244Publisher landing page
- PDF URL
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https://mental.jmir.org/2022/3/e27244/PDFDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://mental.jmir.org/2022/3/e27244/PDFDirect OA link when available
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PsycINFO, Generalizability theory, Mental health, MEDLINE, Social media, Depression (economics), Psychology, Inclusion (mineral), Public health, Computer science, Artificial intelligence, Machine learning, Data science, Medicine, Psychiatry, World Wide Web, Social psychology, Developmental psychology, Political science, Law, Macroeconomics, Economics, NursingTop concepts (fields/topics) attached by OpenAlex
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91Total citation count in OpenAlex
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2025: 32, 2024: 34, 2023: 14, 2022: 11Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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