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Enhanced word embeddings for anorexia nervosa detection on social media

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dc.contributor.author Ramíırez-Cifuentes, Diana
dc.contributor.author Largeron, Christine
dc.contributor.author Tissier, Julien
dc.contributor.author Freire, Ana
dc.contributor.author Baeza Yates, Ricardo
dc.date.accessioned 2020-04-28T10:14:28Z
dc.date.available 2020-04-28T10:14:28Z
dc.date.issued 2020
dc.identifier.citation Ramírez-Cifuentes D, Largeron C, Tissier J, Freire A, Baeza-Yates R. Enhanced word embeddings for anorexia nervosa detection on social media. In: Berthold M, Feelders A, Krempl G, editors. Advances in Intelligent Data Analysis XVIII. 18th International Symposium on Intelligent Data Analysis, IDA 2020 Proceedings; 2020 Apr 27-29; Konstanz, Germany. Cham: Springer; 2020. p. 404-17. (LNCS; no. 12080). DOI: 10.1007/978-3-030-44584-3_32
dc.identifier.isbn 978-3-030-44583-6
dc.identifier.isbn 978-3-030-44584-3
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10230/44357
dc.description Comunicació presentada a: The18th International Symposium on Intelligent Data Analysis, IDA 2020, celebrat del 27 al 29 d'abril de 2020 a Konstanz, Alemanya.
dc.description.abstract Anorexia Nervosa (AN) is a serious mental disorder that has been proved to be traceable on social media through the analysis of users’ written posts. Here we present an approach to generate word embeddings enhanced for a classification task dedicated to the detection of Reddit users with AN. Our method extends Word2vec’s objective function in order to put closer domain-specific and semantically related words. The approach is evaluated through the calculation of an average similarity measure, and via the usage of the embeddings generated as features for the AN screening task. The results show that our method outperforms the usage of fine-tuned pre-learned word embeddings, related methods dedicated to generate domain adapted embeddings, as well as representations learned on the training set using Word2vec. This method can potentially be applied and evaluated on similar tasks that can be formalized as document categorization problems. Regarding our use case, we believe that this approach can contribute to the development of proper automated detection tools to alert and assist clinicians.
dc.description.sponsorship This work was supported by the University of Lyon - IDEXLYON and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Berthold M, Feelders A, Krempl G, editors. Advances in Intelligent Data Analysis XVIII. 18th International Symposium on Intelligent Data Analysis, IDA 2020 Proceedings; 2020 Apr 27-29; Konstanz, Germany. Cham: Springer; 2020. p. 404-17. (LNCS; no. 12080)
dc.rights This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Enhanced word embeddings for anorexia nervosa detection on social media
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1007/978-3-030-44584-3_32
dc.subject.keyword Social media
dc.subject.keyword Eating disorders
dc.subject.keyword Word embeddings
dc.subject.keyword Anorexia nervosa
dc.subject.keyword Representation learning
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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