Welcome to the UPF Digital Repository

Towards suicide prevention: early detection of depression on social media

Show simple item record

dc.contributor.author Leiva, Víctor
dc.contributor.author Freire, Ana
dc.date.accessioned 2017-11-23T11:39:45Z
dc.date.issued 2017
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.uri http://hdl.handle.net/10230/33315
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/pdf
dc.language.iso eng
dc.publisher Springer
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.title Towards suicide prevention: early detection of depression on social media
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-70284-1_34
dc.subject.keyword Early detection
dc.subject.keyword Depression
dc.subject.keyword Social media
dc.subject.keyword Machine learning
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking