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        <title>Predicting Sentence-Level Factuality of News and Bias of Media Outlets</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>Automated news credibility and fact-checking at scale require accurate prediction of news factuality and media bias. This paper introduces a large sentence-level dataset, titled “FactNews”, composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, the fine-grained analysis of subjectivity and impartiality of news articles showed promising results for predicting the reliability of entire media outlets. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, both dataset and baseline were proposed for Brazilian Portuguese.</abstract>
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%0 Conference Proceedings
%T Predicting Sentence-Level Factuality of News and Bias of Media Outlets
%A Vargas, Francielle
%A Jaidka, Kokil
%A Pardo, Thiago
%A Benevenuto, Fabrício
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F vargas-etal-2023-predicting
%X Automated news credibility and fact-checking at scale require accurate prediction of news factuality and media bias. This paper introduces a large sentence-level dataset, titled “FactNews”, composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, the fine-grained analysis of subjectivity and impartiality of news articles showed promising results for predicting the reliability of entire media outlets. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, both dataset and baseline were proposed for Brazilian Portuguese.
%U https://aclanthology.org/2023.ranlp-1.127
%P 1197-1206

Markdown (Informal)

[Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://aclanthology.org/2023.ranlp-1.127) (Vargas et al., RANLP 2023)

• Predicting Sentence-Level Factuality of News and Bias of Media Outlets (Vargas et al., RANLP 2023)

ACL

• Francielle Vargas, Kokil Jaidka, Thiago Pardo, and Fabrício Benevenuto. 2023. Predicting Sentence-Level Factuality of News and Bias of Media Outlets. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1197–1206, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.