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Spanish MTLHateCorpus 2023: multi-task learning for hate speech detection to identify speech type, target, target group and intensity

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dc.contributor.authorRonghao Pan
dc.contributor.authorGarcía Díaz, José Antonio
dc.contributor.authorValencia García, Rafael
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-14T11:42:38Z
dc.date.available2026-01-14T11:42:38Z
dc.date.copyright© 2025 The Authors
dc.date.issued2025-08
dc.description.abstractThe rise of digital communication has exacerbated the challenge of tackling harmful speech online, particularly hate speech, which dehumanises individuals or groups on the basis of traits such as race, gender or ethnicity. This study highlights the urgent need for fine-grained detection methods that take into account several subtasks of hate speech detection, including its intensity, determining the groups to which hate speech is directed, and whether the target is an individual or a group. Furthermore, there is a gap in comprehensive Spanish language corpora that cover these subtasks of hate speech detection. Therefore, we created a novel corpus entitled Spanish MTLHateCorpus 2023 to facilitate the analysis of hate speech in these subtasks and evaluated the effectiveness of the multi-task learning strategy evaluating mBART and T5, comparing its results with other Large Language Models using Zero-Shot Learning as a lower bound and an ensemble based on the mode of several Fine-Tuning as an upper bound. The results achieved by the Multi-Task Learning strategy demonstrated its potential to increase model versatility, allowing a single model to effectively tackle multiple tasks while achieving competitive results, particularly in target group recognition. However, the ensemble learning slightly outperforms the Multi-Task Learning strategy.
dc.formatapplication/pdf
dc.format.extent16
dc.identifier.citation Computer Standards & Interfaces, 2025, Vol. 94 : 103990
dc.identifier.doihttps://doi.org/10.1016/j.csi.2025.103990
dc.identifier.eissn1872-7018
dc.identifier.issn0920-5489
dc.identifier.urihttp://hdl.handle.net/10201/186870
dc.languageeng
dc.publisherElsevier
dc.relationThis work is part of the research project LT-SWM (TED2021-131167B-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. This work is also part of the research project LaTe4PoliticES (PID2022-138099OB-I00) funded by MCIN/AEI/10.13039/501100011033 and the European Fund for Regional Development (ERDF)–a way to make Europe, and the research project ‘‘Services based on language technologies for political microtargeting’’ (22252/PDC/23) funded by the Autonomous Community of the Region of Murcia through the Regional Support Program for the Transfer and Valorization of Knowledge and Scientific Entrepreneurship of the Seneca Foundation, Science and Technology Agency of the Region of Murcia. Mr. Ronghao Pan is supported by the Programa Investigo grant, funded by the Region of Murcia, the Spanish Ministry of Labour and Social Economy and the European Union - NextGenerationEU under the ‘‘Plan de Recuperación, Transformación y Resiliencia (PRTR)’’
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0920548925000194?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHate speech
dc.subjectMulti task learning
dc.subjectZero Shot Learning
dc.subjectFine tuning
dc.subjectText classification
dc.subjectNatural language processing
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleSpanish MTLHateCorpus 2023: multi-task learning for hate speech detection to identify speech type, target, target group and intensity
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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relation.isAuthorOfPublicationab591422-699c-4535-8e8f-fd09f0e90ec2
relation.isAuthorOfPublication.latestForDiscovery14ca7de1-eef1-42b4-9649-b765516ea4f3
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