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dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_AR
dc.contributor.authorGravano, Agustínes_AR
dc.contributor.authorPérez, Juan Manueles_AR
dc.contributor.authorLuque, Franco Mes_AR
dc.contributor.authorZeyat, Demiánes_AR
dc.contributor.authorKondratzky, Martínes_AR
dc.contributor.authorMoro, Agustínes_AR
dc.contributor.authorSerrati, Pablo Santiagoes_AR
dc.contributor.authorZajac, Joaquínes_AR
dc.contributor.authorMiguel, Paulaes_AR
dc.contributor.authorDebandi, Nataliaes_AR
dc.contributor.authorCotik, Vivianaes_AR
dc.date.accessioned2023-05-31T18:56:49Z
dc.date.available2023-05-31T18:56:49Z
dc.date.issued2023
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/11849
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2023.3258973
dc.description.abstractSocial networks and other digital media deal with huge amounts of user-generated contents where hate speech has become a problematic more and more relevant. A great effort has been made to develop automatic tools for its analysis and moderation, at least in its most threatening forms, such as in violent acts against people and groups protected by law. One limitation of current approaches to automatic hate speech detection is the lack of context. The spotlight on isolated messages, without considering any type of conversational context or even the topic being discussed, severely restricts the available information to determine whether a post on a social network should be tagged as hateful or not. In this work, we assess the impact of adding contextual information to the hate speech detection task.We specifically study a subdomain of Twitter data consisting of replies to digital newspapers posts, which provides a natural environment for contextualized hate speech detection. We built a new corpus in Spanish (Rioplatense variant) focused on hate speech associated to the COVID-19 pandemic, annotated using guidelines carefully designed by our interdisciplinary team. Our classification experiments using state-of-the-art transformer-based machine learning techniques show evidence that adding contextual information improves the performance of hate speech detection for two proposed tasks: binary and multi-label prediction, increasing their Macro F1 by 4.2 and 5.5 points, respectively. These results highlight the importance of using contextual information in hate speech detection. Our code, models, and corpus has been made available for further research.es_AR
dc.description.sponsorshipEste artículo se encuentra publicado en IEEE Access, 11, 30575-30590.es_AR
dc.format.extentpp. 30575-30590es_AR
dc.format.mediumapplication/pdfes_AR
dc.languageenges_AR
dc.relation.ispartofIEEE Access, vol. 11, pp. 30575-30590, 2023, doi: 10.1109/ACCESS.2023.3258973.
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectNLPes_AR
dc.subjectText classificationes_AR
dc.subjectHate speech detectiones_AR
dc.subjectContextual informationes_AR
dc.subjectSpanish corpuses_AR
dc.subjectCovid-19 hate speecheses_AR
dc.titleAssessing the Impact of Contextual Information in Hate Speech Detectiones_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_AR


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