Towards suicide prevention: early detection of depression on social media

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• dc.contributor.author Leiva, Víctorca
• dc.contributor.author Freire, Anaca
• dc.date.accessioned 2017-11-23T11:39:45Z
• dc.date.issued 2017
• dc.description.abstract The statistics presented by the World Health Organization inform that 90% of the suicides can be attributed to mental illnesses in high-income countries. Besides, previous studies concluded that people with mental illnesses tend to reveal their mental condition on social media, as a way of relief. Thus, the main objective of this work is the analysis of the messages that a user posts online, sequentially through a time period, and detect as soon as possible if this user is at risk of depression. This paper is a preliminary attempt to minimize measures that penalize the delay in detecting positive cases. Our experiments underline the importance of an exhaustive sentiment analysis and a combination of learning algorithms to detect early symptoms of depression.
• dc.description.sponsorship This work was supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdfca
• dc.identifier.citation Leiva V, Freire A. Towards suicide prevention: early detection of depression on social media. In: Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D, editors. Internet Science: 4th International Conference, INSCI 2017, Thessaloniki, Greece, November 22-24, 2017, Proceedings. [Germany]: Springer, 2017. p. 428-36. (LNCS; no. 10673). DOI: 10.1007/978-3-319-70284-1_34
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-70284-1_34
• dc.identifier.uri http://hdl.handle.net/10230/33315
• dc.language.iso eng
• dc.publisher Springerca
• dc.relation.ispartof Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D, editors. Internet Science: 4th International Conference, INSCI 2017, Thessaloniki, Greece, November 22-24, 2017, Proceedings. [Germany]: Springer, 2017. p. 428-36. (LNCS; no. 10673).
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org//10.1007/978-3-319-70284-1_34
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Early detectionen
• dc.subject.keyword Depressionen
• dc.subject.keyword Social mediaen
• dc.subject.keyword Machine learningen
• dc.title Towards suicide prevention: early detection of depression on social mediaen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion