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

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dc.citedby0
dc.contributor.authorMat Ripah N.A.en_US
dc.contributor.authorAbdul Latif A.en_US
dc.contributor.authorChe Cob Z.en_US
dc.contributor.authorMohd Drus S.en_US
dc.contributor.authorMd Anwar R.en_US
dc.contributor.authorMohd Radzi H.en_US
dc.contributor.authorid58307856000en_US
dc.contributor.authorid58113291000en_US
dc.contributor.authorid58308287800en_US
dc.contributor.authorid57894000400en_US
dc.contributor.authorid58309156100en_US
dc.contributor.authorid57211279880en_US
dc.date.accessioned2024-10-14T03:20:52Z
dc.date.available2024-10-14T03:20:52Z
dc.date.issued2023
dc.description.abstractPeople 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-19-8406-8_14
dc.identifier.epage203
dc.identifier.scopus2-s2.0-85161461583
dc.identifier.spage193
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161461583&doi=10.1007%2f978-981-19-8406-8_14&partnerID=40&md5=72cd01aec0fe74c48e3d8886476df587
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34585
dc.identifier.volume983 LNEE
dc.pagecount10
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Electrical Engineering
dc.subjectDepression detection
dc.subjectMachine learning
dc.subjectSocial media
dc.subjectDiseases
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectPatient treatment
dc.subjectSocial networking (online)
dc.subjectSupport vector machines
dc.subjectDepression detection
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectMental disease
dc.subjectMental health
dc.subjectMental illness
dc.subjectMental problems
dc.subjectSocial media
dc.subjectSocial media analytics
dc.subjectSystematic literature review
dc.subjectDiagnosis
dc.titleDepression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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