  Automated news credibility and fact-checking at scale require accurately
predicting 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
provided 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 were
proposed for Brazilian Portuguese.

Computation and Language