Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review

People are becoming more conscious of the importance of mental health as time goes on. Thus, the detection of mental diseases is becoming a significant concern. Due to the multifaceted nature of each mental problem, many psychiatrists have had difficulty diagnosing mental illness in a patient, makin...

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Main Authors: Mat Ripah N.A., Abdul Latif A., Che Cob Z., Mohd Drus S., Md Anwar R., Mohd Radzi H.
Other Authors: 58307856000
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2024
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spelling my.uniten.dspace-345852024-10-14T11:20:52Z Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review Mat Ripah N.A. Abdul Latif A. Che Cob Z. Mohd Drus S. Md Anwar R. Mohd Radzi H. 58307856000 58113291000 58308287800 57894000400 58309156100 57211279880 Depression detection Machine learning Social media Diseases Learning algorithms Learning systems Patient treatment Social networking (online) Support vector machines Depression detection Machine learning algorithms Machine-learning Mental disease Mental health Mental illness Mental problems Social media Social media analytics Systematic literature review Diagnosis People are becoming more conscious of the importance of mental health as time goes on. Thus, the detection of mental diseases is becoming a significant concern. Due to the multifaceted nature of each mental problem, many psychiatrists have had difficulty diagnosing mental illness in a patient, making it challenging to provide proper therapy before it is too late. However, because social media has become so ingrained in people's daily lives, it has created an environment where more information about a patient's mental illness is potentially available. This research was carried out as a Systematic Literature Review (SLR), a method of locating, evaluating, and interpreting publicly available materials to answer a set of research questions. The purpose of this study is to answer questions about text-based depression detection based on people who depressive behavior might have shown in their social media postings. The findings reveal that specific aspects of how these people use social media can help diagnose depression early on. This SLR discovered that the chosen social media data is basically the country's leading social site. However, some of the papers indirectly mentioned their challenges during the process. The main challenges highlighted are regarding the ethical issues of the data available. Furthermore, it is also shown that various machine learning algorithms are used, and the most used are Neural Network and Support Vector Machine. Similarly, the most common computing tool used is Phyton. The use of social media, as well as computational tools and machine learning algorithm, contributes to current public health efforts to detect any indicators of depression from sources close to patients. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Final 2024-10-14T03:20:52Z 2024-10-14T03:20:52Z 2023 Conference Paper 10.1007/978-981-19-8406-8_14 2-s2.0-85161461583 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161461583&doi=10.1007%2f978-981-19-8406-8_14&partnerID=40&md5=72cd01aec0fe74c48e3d8886476df587 https://irepository.uniten.edu.my/handle/123456789/34585 983 LNEE 193 203 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Depression detection
Machine learning
Social media
Diseases
Learning algorithms
Learning systems
Patient treatment
Social networking (online)
Support vector machines
Depression detection
Machine learning algorithms
Machine-learning
Mental disease
Mental health
Mental illness
Mental problems
Social media
Social media analytics
Systematic literature review
Diagnosis
spellingShingle Depression detection
Machine learning
Social media
Diseases
Learning algorithms
Learning systems
Patient treatment
Social networking (online)
Support vector machines
Depression detection
Machine learning algorithms
Machine-learning
Mental disease
Mental health
Mental illness
Mental problems
Social media
Social media analytics
Systematic literature review
Diagnosis
Mat Ripah N.A.
Abdul Latif A.
Che Cob Z.
Mohd Drus S.
Md Anwar R.
Mohd Radzi H.
Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
description People are becoming more conscious of the importance of mental health as time goes on. Thus, the detection of mental diseases is becoming a significant concern. Due to the multifaceted nature of each mental problem, many psychiatrists have had difficulty diagnosing mental illness in a patient, making it challenging to provide proper therapy before it is too late. However, because social media has become so ingrained in people's daily lives, it has created an environment where more information about a patient's mental illness is potentially available. This research was carried out as a Systematic Literature Review (SLR), a method of locating, evaluating, and interpreting publicly available materials to answer a set of research questions. The purpose of this study is to answer questions about text-based depression detection based on people who depressive behavior might have shown in their social media postings. The findings reveal that specific aspects of how these people use social media can help diagnose depression early on. This SLR discovered that the chosen social media data is basically the country's leading social site. However, some of the papers indirectly mentioned their challenges during the process. The main challenges highlighted are regarding the ethical issues of the data available. Furthermore, it is also shown that various machine learning algorithms are used, and the most used are Neural Network and Support Vector Machine. Similarly, the most common computing tool used is Phyton. The use of social media, as well as computational tools and machine learning algorithm, contributes to current public health efforts to detect any indicators of depression from sources close to patients. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
author2 58307856000
author_facet 58307856000
Mat Ripah N.A.
Abdul Latif A.
Che Cob Z.
Mohd Drus S.
Md Anwar R.
Mohd Radzi H.
format Conference Paper
author Mat Ripah N.A.
Abdul Latif A.
Che Cob Z.
Mohd Drus S.
Md Anwar R.
Mohd Radzi H.
author_sort Mat Ripah N.A.
title Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
title_short Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
title_full Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
title_fullStr Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
title_full_unstemmed Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
title_sort depression detection based on features of depressive behaviour through social media analytic: a systematic literature review
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814060105059008512
score 13.209306