A machine learning-based depression detection on social media platforms for adolescents: A work in progress narrative review

Vahdati, F; Atif, A; Saberi, M

A machine learning-based depression detection on social media platforms for adolescents: A work in progress narrative review

Vahdati, F Atif, A

Saberi, M

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Publication Type:: Conference Proceeding
Citation:: 2022, pp. 1-6
Issue Date:: 2022-12-16

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Field	Value	Language
dc.contributor.author	Vahdati, F
dc.contributor.author	Atif, A https://orcid.org/0000-0003-0381-0343
dc.contributor.author	Saberi, M
dc.date	2022-12-14
dc.date.accessioned	2023-02-24T06:25:32Z
dc.date.available	2022-09-27
dc.date.available	2023-02-24T06:25:32Z
dc.date.issued	2022-12-16
dc.identifier.citation	2022, pp. 1-6
dc.identifier.uri	http://hdl.handle.net/10453/166419
dc.description.abstract	A rise in depression episodes has prompted an increased focus on depression detection. This research paper aims to review the literature to discover the pros and cons of proposed solutions for this critical social problem. In this narrative review, specifically, we looked at machine learning (ML) based techniques that analyse text data from social media to diagnose depression symptoms. A thorough search technique across several databases for relevant articles, specifically Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed databases, were used to perform a narrative evaluation. Terms and definitions were used to filter the article titles, abstracts, and full texts. Approaches based on machine learning and text data from social media may be helpful in the diagnosis of depression and might be used in conjunction with other mental health services.
dc.language	en
dc.relation.ispartof	Asia-Pacific Conference on Computer Science and Data Engineering
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A machine learning-based depression detection on social media platforms for adolescents: A work in progress narrative review
dc.type	Conference Proceeding
utslib.location.activity	Gold Coast
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2023-02-24T06:25:30Z
pubs.finish-date	2022-12-16
pubs.publication-status	Accepted
pubs.start-date	2022-12-14

Abstract:

A rise in depression episodes has prompted an increased focus on depression detection. This research paper aims to review the literature to discover the pros and cons of proposed solutions for this critical social problem. In this narrative review, specifically, we looked at machine learning (ML) based techniques that analyse text data from social media to diagnose depression symptoms. A thorough search technique across several databases for relevant articles, specifically Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed databases, were used to perform a narrative evaluation. Terms and definitions were used to filter the article titles, abstracts, and full texts. Approaches based on machine learning and text data from social media may be helpful in the diagnosis of depression and might be used in conjunction with other mental health services.

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http://hdl.handle.net/10453/166419