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Building a framework for fake news detection in the health domain

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dc.contributor.authorMartinez Rico, Juan R.
dc.contributor.authorAraujo Serna, M. Lourdes
dc.contributor.authorMartínez Romo, Juan
dc.contributor.editorBongelli, Ramona
dc.date.accessioned2024-07-29T08:56:00Z
dc.date.available2024-07-29T08:56:00Z
dc.date.issued2024-07-08
dc.description.abstractDisinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary Classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.en
dc.description.versionversión publicada
dc.identifier.citationMartinez-Rico JR, Araujo L, Martinez- Romo J (2024) Building a framework for fake news detection in the health domain. PLoS ONE 19(7): e0305362; https://doi.org/10.1371/journal.pone.0305362
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0305362
dc.identifier.issn1932-6203; e-ISSN:1932-6203
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23138
dc.journal.issue7
dc.journal.titlePLoS ONE
dc.journal.volume19
dc.language.isoes
dc.publisherSan Francisco CA: Public Library of Science
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.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.titleBuilding a framework for fake news detection in the health domainen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication77c4023e-4374-442a-9dfb-b9d4b609c31e
relation.isAuthorOfPublication91b7e317-2a30-494f-98e9-3a0e026747b1
relation.isAuthorOfPublication.latestForDiscovery91b7e317-2a30-494f-98e9-3a0e026747b1
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