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Evaluating feature combination strategies for hate-speech detection in Spanish using linguistic features and transformers

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dc.contributor.authorGarcía Díaz, José Antonio
dc.contributor.authorJiménez Zafra, Salud María
dc.contributor.authorGarcía Cumbreras, Miguel Ángel
dc.contributor.authorValencia García, Rafael
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-14T11:53:06Z
dc.date.available2026-01-14T11:53:06Z
dc.date.copyright© 2022 The Authors
dc.date.issued2023
dc.description.abstractThe rise of social networks has allowed misogynistic, xenophobic, and homophobic people to spread their hate-speech to intimidate individuals or groups because of their gender, ethnicity or sexual orientation. The consequences of hate-speech are devastating, causing severe depression and even leading people to commit suicide. Hate-speech identification is challenging as the large amount of daily publications makes it impossible to review every comment by hand. Moreover, hate-speech is also spread by hoaxes that requires language and context understanding. With the aim of reducing the number of comments that should be reviewed by experts, or even for the development of autonomous systems, the automatic identification of hate-speech has gained academic relevance. However, the reliability of automatic approaches is still limited specifically in languages other than English, in which some of the state-of-the-art techniques have not been analyzed in detail. In this work, we examine which features are most effective in identifying hate-speech in Spanish and how these features can be combined to develop more accurate systems. In addition, we characterize the language present in each type of hate-speech by means of explainable linguistic features and compare our results with state-of-the-art approaches. Our research indicates that combining linguistic features and transformers by means of knowledge integration outperforms current solutions regarding hate-speech identification in Spanish.
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dc.format.extent22
dc.identifier.citationComplex & Intelligent Systems, 2023, Vol. 9, pp. 2893–2914
dc.identifier.doihttps://doi.org/10.1007/s40747-022-00693-x
dc.identifier.eissn2198-6053
dc.identifier.issn2199-4536
dc.identifier.urihttp://hdl.handle.net/10201/186890
dc.languageeng
dc.publisherSpringer
dc.relationThis work was supported by project LaTe4PSP (PID2019-107652RB-I00) funded byMCIN/AEI/10.13039/501100011 033, Project AlInFunds (PDC2021-121112-I00) funded byMCIN/AEI/ 10.13039/501100011033 and by the European Union NextGenerationEU/ PRTR, Project LIVING-LANG (RTI2018-094653-B-C21) funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe, Project PID2020-116118GA-I00 supported by MICINN/AEI/10.13039/501100011033, Project PID2020-119478GBI00 supported by MICINN/AEI/10.13039/501100011033, Banco Santander and University of Murcia through the industrial doctorate program, Fondo Social Europeo and Administration of the Junta de Andalucía (DOC_01073), Grant P20_00956 (PAIDI 2020) from the Andalusian Regional Government and grant 1380939 (FEDER Andalucía 2014-2020) from the Andalusian Regional Government.
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s40747-022-00693-x
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHate speech
dc.subjectFeature engineering
dc.subjectKnowledge integration
dc.subjectText classification
dc.subjectNatural language processing
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleEvaluating feature combination strategies for hate-speech detection in Spanish using linguistic features and transformers
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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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