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The increasing prevalence of political bias in news media calls for greaterpublic awareness of it, as well as robust methods for its detection. Whileprior work in NLP has primarily focused on the lexical bias captured bylinguistic attributes such as word choice and syntax, other types of bias stemfrom the actual content selected for inclusion in the text. In this work, weinvestigate the effects of informational bias: factual content that cannevertheless be deployed to sway reader opinion. We first produce a newdataset, BASIL, of 300 news articles annotated with 1,727 bias spans and findevidence that informational bias appears in news articles more frequently thanlexical bias. We further study our annotations to observe how informationalbias surfaces in news articles by different media outlets. Lastly, a baselinemodel for informational bias prediction is presented by fine-tuning BERT on ourlabeled data, indicating the challenges of the task and future directions.

In Plain Sight: Media Bias Through the Lens of Factual Reporting