All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
This addresses the issue of media bias for researchers and the public by uncovering subtle forms of ideological influence through event selection, though it is incremental as it builds on existing methods for bias detection.
The paper tackles the problem of detecting partisan events in news media by developing a latent variable-based framework that compares multiple articles on the same story to identify events whose inclusion or omission reveals ideological bias. The results show that this cross-article comparison method outperforms competitive baselines in detecting partisan events and article ideology, revealing bias even in mainstream media with norms of objectivity.
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which 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-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines. Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship. Our codebase and dataset are available at https://github.com/launchnlp/ATC.