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The manifestation and effect of bias in news reporting have been centraltopics in the social sciences for decades, and have received increasingattention in the NLP community recently. While NLP can help to scale upanalyses or contribute automatic procedures to investigate the impact of biasednews in society, we argue that methodologies that are currently dominant fallshort of addressing the complex questions and effects addressed in theoreticalmedia studies. In this survey paper, we review social science approaches anddraw a comparison with typical task formulations, methods, and evaluationmetrics used in the analysis of media bias in NLP. We discuss open questionsand suggest possible directions to close identified gaps between theory andpredictive models, and their evaluation. These include model transparency,considering document-external information, and cross-document reasoning ratherthan single-label assignment.

Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing