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dc.contributor.advisorBarone, Dante Augusto Coutopt_BR
dc.contributor.authorAmantea, Rafael Pinheiropt_BR
dc.date.accessioned2021-07-10T04:52:32Zpt_BR
dc.date.issued2021pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/223599pt_BR
dc.description.abstractThe spreading of fake news is a reality within modern times. However, in the daily fight against disinformation, the fact-checking agencies are one of the strongest allies. Some techniques have been in place to help in this battle, and one of them is the ClaimReview web markup, which had been introduced to grant access to fact-checking articles meaning by search engines. Despite its importance within this context, barely half of the fact-checkers have adopted it. Therefore, in this work, we provide a starting point for the automatic generation of ClaimReview markup, investigating means to predict Claim- Review’s attributes using machine learning models. By experimenting and comparing the baseline approach, Support Vector Machine, with the state-of-the-art (BERT) we have achieved noticeable results, creating a benchmark for upcoming researches in this domain.pt_BR
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.rightsOpen Accessen
dc.subjectFake newsen
dc.subjectAprendizado : máquinapt_BR
dc.subjectClaimReviewen
dc.subjectSVMen
dc.subjectBERTen
dc.titleA comparison of machine learning approaches for predicting ClaimReview markup attributes from fact-checking websitespt_BR
dc.typeTrabalho de conclusão de graduaçãopt_BR
dc.contributor.advisor-coCôrtes, Eduardo Gabrielpt_BR
dc.identifier.nrb001127671pt_BR
dc.degree.grantorUniversidade Federal do Rio Grande do Sulpt_BR
dc.degree.departmentInstituto de Informáticapt_BR
dc.degree.localPorto Alegre, BR-RSpt_BR
dc.degree.date2020pt_BR
dc.degree.graduationCiência da Computação: Ênfase em Ciência da Computação: Bachareladopt_BR
dc.degree.levelgraduaçãopt_BR


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