All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

Anonymous

Abstract: Public opinion is shaped by the information news media provide, and that information may be shaped by ideological leaning of media outlets. While much attention has been devoted to media bias via overt ideological language, a more profound way the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other. We develop a latent variable model to predict news articles' ideology and identify partisan events by same-story article comparison. Our study validates the existence of partisan event selection and shows that cross-document comparison helps detect partisan events and article ideology. Our results reveal a high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship.

Paper Type: short

Research Area: Computational Social Science and Cultural Analytics