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Detecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings

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dc.contributor.authorGarcía Díaz, José Antonio
dc.contributor.authorCánovas-García, Mar
dc.contributor.authorColomo-Palacios, Ricardo
dc.contributor.authorValencia García, Rafael
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultad de Informática
dc.date.accessioned2026-01-16T08:19:08Z
dc.date.available2026-01-16T08:19:08Z
dc.date.copyright© 2020, Elsevier B.V. All rights reserved
dc.date.issued2020-08-22
dc.description.abstractOnline social networks allow powerless people to gain enormous amounts of control over particular people's lives and pro t from the anonymity or social distance that the Internet provides in order to harass other people. One of the most frequently targeted groups comprise women, as misogyny is, unfortunately, a reality in our society. However, although great e orts have recently been made to identify misogyny, it is still di cult to distinguish as it can sometimes be very subtle and deep, signifying that the use of statistical approaches is not su cient. Moreover, as Spanish is spoken worldwide, context and cultural di erences can complicate this identi cation. Our contribution to the detection of misogyny in Spanish is two-fold. On the one hand, we apply Sentiment Analysis and Social Computing technologies for detecting misogynous messages in Twitter. On the other, we have compiled the Spanish MisoCorpus-2020, a balanced corpus regarding misogyny in Spanish, and classi ed it into three subsets concerning (1) violence towards relevant women, (2) messages harassing women in Spanish from Spain and Spanish from Latin America, and (3) general traits related to misogyny. Our proposal combines a classi cation based on average word embeddings and linguistic features in order to understand which linguistic phenomena principally contribute to the identi cation of misogyny. We have evaluated our proposal with three machine-learning classi ers, achieving the best accuracy of 85.175%. Finally the proposed approach is also validated with existing corpora for misogyny and aggressiveness detection such as AMI and HatEval obtaining good results
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dc.format.extent32
dc.identifier.citationJosé Antonio García-Díaz, Mar Cánovas-García, Ricardo Colomo-Palacios, Rafael Valencia-García, Detecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings, Future Generation Computer Systems, Volume 114, 2021, Pages 506-518, ISSN 0167-739X, https://doi.org/10.1016/j.future.2020.08.032
dc.identifier.doihttps://doi.org/10.1016/j.future.2020.08.032
dc.identifier.eissn1872-7115
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/10201/187629
dc.languageeng
dc.publisherElsevier
dc.relationThis work has been supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through projects KBS4FIA (TIN2016-76323-R) and LaTe4PSP (PID2019-107652RB-I00). In addition, José Antonio García-Díaz has been supported by Banco Santander and University of Murcia, Spain through the Doctorado industrial programme.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X20301928?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMisogyny detection
dc.subjectText classification
dc.subjectNatural language processing
dc.subjectMachine-learning
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleDetecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication14ca7de1-eef1-42b4-9649-b765516ea4f3
relation.isAuthorOfPublicationab591422-699c-4535-8e8f-fd09f0e90ec2
relation.isAuthorOfPublication.latestForDiscovery14ca7de1-eef1-42b4-9649-b765516ea4f3
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