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Together we can do it! A roadmap to effectively tackle propaganda-related tasks

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dc.contributor.authorRodríguez García, Raquel
dc.contributor.authorCenteno Sánchez, Roberto
dc.contributor.authorRodrigo Yuste, Álvaro
dc.date.accessioned2024-11-25T12:49:52Z
dc.date.available2024-11-25T12:49:52Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in “ Internet Research, 2024", is available online at the publisher's website: Emerald, https://doi.org/10.1108/INTR-05-2024-0785 La versión registrada de este artículo, publicado por primera vez en “Internet Research, 2024", está disponible en línea en el sitio web del editor: Emerald, https://doi.org/10.1108/INTR-05-2024-0785
dc.description.abstractPurpose In this paper, we address the need to study automatic propaganda detection to establish a course of action when faced with such a complex task. Although many isolated tasks have been proposed, a roadmap on how to best approach a new task from the perspective of text formality or the leverage of existing resources has not been explored yet. Design/methodology/approach We present a comprehensive study using several datasets on textual propaganda and different techniques to tackle it. We explore diverse collections with varied characteristics and analyze methodologies, from classic machine learning algorithms, to multi-task learning to utilize the available data in such models. Findings Our results show that transformer-based approaches are the best option with high-quality collections, and emotionally enriched inputs improve the results for Twitter content. Additionally, MTL achieves the best results in two of the five scenarios we analyzed. Notably, in one of the scenarios, the model achieves an F1 score of 0.78, significantly surpassing the transformer baseline model’s F1 score of 0.68. Research limitations/implications After finding a positive impact when leveraging propaganda’s emotional content, we propose further research into exploiting other complex dimensions, such as moral issues or logical reasoning. Originality/value Based on our findings, we provide a roadmap for tackling propaganda-related tasks, depending on the types of training data available and the task to solve. This includes the application of MTL, which has yet to be fully exploited in propaganda detection.en
dc.description.versionversión final
dc.identifier.citationRodríguez-García, R., Centeno, R. and Rodrigo, Á. (2024), "Together we can do it! A roadmap to effectively tackle propaganda-related tasks", Internet Research, Vol. ahead-of-print No. ahead-of-print, pp. 1-24. https://doi.org/10.1108/INTR-05-2024-0785
dc.identifier.doihttps://doi.org/10.1108/INTR-05-2024-0785
dc.identifier.issn1066-2243 | eISSN 2054-5657
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24508
dc.journal.titleInternet Research
dc.language.isoen
dc.page.final24
dc.page.initial1
dc.publisherEmerald
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-nc/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.titleTogether we can do it! A roadmap to effectively tackle propaganda-related tasksen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscoveryf724984c-01b1-4e8d-810e-1b107c938615
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