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Leveraging Unsupervised Task Adaptation and Semi-Supervised Learning With Semantic-Enriched Representations for Online Sexism Detection

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dc.contributor.authorRodríguez-Sánchez, Francisco
dc.contributor.authorCarrillo de Albornoz Cuadrado, Jorge Amando
dc.contributor.authorPlaza Morales, Laura
dc.contributor.orcidhttps://orcid.org/0000-0002-4669-5261
dc.date.accessioned2025-02-20T18:24:01Z
dc.date.available2025-02-20T18:24:01Z
dc.date.issued2024-10-25
dc.descriptionThis is an Accepted Manuscript of an article published by Wiley in "Expert Systems, 2025; 42:e13763", available at: https://doi.org/10.1111/exsy.13763 Este es el manuscrito aceptado del artículo publicado por Wiley en "Expert Systems, 2025; 42:e13763", disponible en línea: https://doi.org/10.1111/exsy.13763
dc.description.abstractOver the past decade, the proliferation of hateful and sexist content targeting women on social media has become a concerning issue, adversely affecting women's lives and freedom of expression. Previous efforts to detect online sexism have utilized monolingual ensemble transformers combined with data augmentation techniques that incorporate related-domain data, such as hate speech. However, these approaches often struggle to capture the full diversity and complexity of sexism due to limitations in the size and quality of training data. In this study, we introduce a novel sexism detection system that employs in-domain unlabeled data through unsupervised task-adaptation techniques and semi-supervised learning, using an efficient single multilingual transformer model. Additionally, we incorporate a Sentence-BERT layer to enhance our system with semantically meaningful sentence embeddings. Our proposed system outperforms existing state-of-the-art methods across all tasks and datasets, demonstrating its effectiveness in detecting and addressing sexism in social media text. These results underscore the potential of our approach, providing a foundation for further research and practical applications.en
dc.description.versionversión final
dc.identifier.citationRodríguez-Sánchez, Francisco Carrillo de Albornoz Cuadrado, Jorge Amando Plaza Morales, Laura. Leveraging Unsupervised Task Adaptation and Semi-Supervised Learning With Semantic-Enriched Representations for Online Sexism Detection, Expert Systems, 2025; 42:e13763 https://doi.org/10.1111/exsy.13763
dc.identifier.doihttps://doi.org/10.1111/exsy.13763
dc.identifier.issnISSN 0266-4720 eISSN 1468-0394
dc.identifier.urihttps://hdl.handle.net/20.500.14468/25947
dc.journal.titleExpert Systems
dc.journal.volume42
dc.language.isoen
dc.publisherWiley
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject61 Psicología::6114 Psicología social
dc.subject33 Ciencias Tecnológicas::3304 Tecnología de los ordenadores
dc.subject.keywordsonline sexismen
dc.subject.keywordssexism detectionen
dc.subject.keywordssexism categorizationen
dc.subject.keywordssocial mediaen
dc.subject.keywordssexism dataseten
dc.titleLeveraging Unsupervised Task Adaptation and Semi-Supervised Learning With Semantic-Enriched Representations for Online Sexism Detectionen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication53880f08-9d78-461b-9d10-89624a089b47
relation.isAuthorOfPublication29746aa9-9544-4364-8665-8b92a433fbfe
relation.isAuthorOfPublication.latestForDiscovery53880f08-9d78-461b-9d10-89624a089b47
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