Early risk detection of anorexia on social media

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• dc.contributor.author Ramíırez-Cifuentes, Diana
• dc.contributor.author Mayans Yern, Marc
• dc.contributor.author Freire, Ana
• dc.date.accessioned 2019-03-05T16:08:50Z
• dc.date.issued 2018
• dc.description Comunicació presentada a: INSCI 2018 celebrada del 24 al 26 d'octubre de 2018 a Sant Petersburg, Rússia.ca
• dc.description.abstract This paper proposes an approach for the early detection of anorexia nervosa (AN) on social media. We present a machine learning approach that processes the texts written by social media users. This method relies on a set of features based on domain-specific vocabulary, topics, psychological processes, and linguistic information extracted from the users’ writings. This approach penalizes the delay in detecting positive cases in order to classify the users in risk as early as possible. Identifying anorexia early, along with an appropriate treatment, improves the speed of recovery and the likelihood of staying free of the illness. The results of this work showed that our proposal is suitable for the early detection of AN symptoms.en
• 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).en
• dc.format.mimetype application/pdf
• dc.identifier.citation Ramírez-Cifuentes D, Mayans M, Freire A. Early risk detection of anorexia on social media. In: Bodrunova S, editor. Internet Science. 5th International Conference, INSCI 2018, Proceedings; 2018 Oct 24-26; St. Petersburg, Russia. Cham: Springer; 2018. p. 3-14. (LNCS; no. 11193. LNISA; no. 11193). DOI: 10.1007/978-3-030-01437-7_1
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-030-01437-7_1
• dc.identifier.isbn 9783030014360
• dc.identifier.issn 0302-9743
• dc.identifier.uri http://hdl.handle.net/10230/36745
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Bodrunova S, editor. Internet Science. 5th International Conference, INSCI 2018, Proceedings; 2018 Oct 24-26; St. Petersburg, Russia. Cham: Springer; 2018. p. 3-14. (LNCS; no. 11193. LNISA; no. 11193).
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-01437-7_1
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Early risk detectionen
• dc.subject.keyword Eating disordersen
• dc.subject.keyword Social mediaen
• dc.subject.keyword Anorexiaen
• dc.subject.keyword Machine learningen
• dc.title Early risk detection of anorexia on social media
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion