Mental disorder and suicidal ideation detection from social media using deep neural networks

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dc.authorid0000-0002-7877-7528
dc.authorid0000-0002-9619-8247
dc.contributor.authorEzerceli, Özayen_US
dc.contributor.authorDehkharghani, Rahimen_US
dc.date.accessioned2025-07-25T12:59:43Z
dc.date.available2025-07-25T12:59:43Z
dc.date.issued2024-12
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.descriptionOpen access funding provided by the Scientific and Technological Research Council of T\u00FCrkiye (T\u00DCB\u0130TAK).en_US
dc.description.abstractDepression and suicidal ideation are global reasons for life-threatening injury and death. Mental disorders have increased especially among young people in recent years, and early detection of those cases can prevent suicide attempts. Social media platforms provide users with an anonymous space to interact with others, making them a secure environment to discuss their mental disorders. This paper proposes a solution to detect depression/suicidal ideation using natural language processing and deep learning techniques. We used Transformers and a unique model to train the proposed model and applied it to three diferent datasets: SuicideDetection, CEASEv2.0, and SWMH. The proposed model is evaluated using the accuracy, precision, recall, and ROC curve. The proposed model outperforms the state-of-theart in the SuicideDetection and CEASEv2.0 datasets, achieving F1 scores of 0.97 and 0.75, respectively. However, in the SWMH data set, the proposed model is 4% points behind the state-of-the-art precision providing the F1 score of 0.68. In the real world, this project could help psychologists in the early detection of depression and suicidal ideation for a more efcient treatment. The proposed model achieves state-of-the-art performance in two of the three datasets, so they could be used to develop a screening tool that could be used by mental health professionals or individuals to assess their own risk of suicide. This could lead to early intervention and treatment, which could save lives.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEzerceli, Ö. & Dehkharghani, R. (2024). Mental disorder and suicidal ideation detection from social media using deep neural networks. Journal of Computational Social Science, 7(3), 2277-2307. doi:10.1007/s42001-024-00307-1en_US
dc.identifier.doi10.1007/s42001-024-00307-1
dc.identifier.endpage2307
dc.identifier.issn2432-2717
dc.identifier.issn2432-2725
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85197700231
dc.identifier.scopusqualityQ2
dc.identifier.startpage2277
dc.identifier.urihttps://hdl.handle.net/11729/6583
dc.identifier.urihttps://doi.org/10.1007/s42001-024-00307-1
dc.identifier.volume7
dc.identifier.wosWOS:001263428000002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakEmerging Sources Citation Index (ESCI)en_US
dc.institutionauthorEzerceli, Özayen_US
dc.institutionauthorid0000-0002-7877-7528
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Computational Social Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSuicidal ideation detectionen_US
dc.subjectSocial media contenten_US
dc.subjectWord embeddingen_US
dc.subjectDeep neural networken_US
dc.subjectBERT transformersen_US
dc.subjectDepressionen_US
dc.titleMental disorder and suicidal ideation detection from social media using deep neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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