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Automated news credibility and fact-checking at scale require accuratelypredicting news factuality and media bias. This paper introduces a largesentence-level dataset, titled "FactNews", composed of 6,191 sentences expertlyannotated according to factuality and media bias definitions proposed byAllSides. We use FactNews to assess the overall reliability of news sources, byformulating two text classification problems for predicting sentence-levelfactuality of news reporting and bias of media outlets. Our experimentsdemonstrate that biased sentences present a higher number of words compared tofactual sentences, besides having a predominance of emotions. Hence, thefine-grained analysis of subjectivity and impartiality of news articlesprovided promising results for predicting the reliability of 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 wereproposed for Brazilian Portuguese.

Predicting Sentence-Level Factuality of News and Bias of Media Outlets