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A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it

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dc.contributor.authorRodrigo Ginés, Francisco Javier
dc.contributor.authorCarrillo de Albornoz Cuadrado, Jorge Amando
dc.contributor.authorPlaza Morales, Laura
dc.contributor.orcidhttps://orcid.org/0000-0001-6235-6860
dc.date.accessioned2024-10-08T14:32:20Z
dc.date.available2024-10-08T14:32:20Z
dc.date.issued2023-09-26
dc.descriptionThe registered version of this article, first published in Expert Systems with Applications, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.eswa.2023.121641
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Expert Systems with Applications, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.eswa.2023.121641
dc.description.abstractMedia bias and the intolerance of media outlets and citizens to deal with opposing points of view pose a threat to the proper functioning of democratic processes. In this respect, we present a systematic review of the literature related to media bias detection, in order to characterize and classify the different types of media bias, and to explore the state-of-the-art of automatic media bias detection systems. The main objectives of this paper were twofold. First, we framed information, misinformation and disinformation within a theoretical framework that allows us to differentiate the different existing misinformation problems such as us media bias, fake news, or propaganda. Second, we studied the state of the art of automatic media bias detection systems: analyzing the most recently used techniques and their results, listing the available resources and the most relevant datasets, and establishing a discussion about how to increase the maturity of this area. After doing a comprehensive literature review, we have identified and selected a total of 17 forms of media bias that can be classified depending on the context (e.g., coverage bias, gatekeeping bias, or statement bias), and on the author’s intention (e.g., spin bias, or ideology bias). We also reviewed, following the PRISMA methodology, the main automatic media bias detection systems that have been developed so far, selecting 63 relevant articles, from which we extracted the most used techniques; including non-deep learning methods (e.g., linguistic-based methods, and reported speech-based methods), and deep learning methods (e.g., RNNs-based methods, and transformers-based methods). Additionally, we listed and summarized 18 available datasets for the task of automatic media bias detection. In conclusion, the current methods for automatic media bias detection are still in their infancy and there is still a lot of potential for improvement in terms of accuracy and robustness. We have proposed some future research lines that could potentially contribute to the development of more advanced techniques.en
dc.description.versionversión final
dc.identifier.citationRodrigo-Ginés, F. J., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it. Expert Systems with Applications, 237, 121641.
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2023.121641
dc.identifier.issn1873-6793
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23961
dc.journal.titleExpert Systems with Applications
dc.journal.volume237
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.relation.researchgroupNatural Language Processing & Information Retrieval Group (NLP&IR)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsNatural Language Processing (NLP)en
dc.subject.keywordsmedia bias detectionen
dc.subject.keywordsinformation theoryen
dc.subject.keywordsdisinformationen
dc.titleA systematic review on media bias detection: What is media bias, how it is expressed, and how to detect iten
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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