All Things Considered: Detecting Partisan Events from News Media with
  Cross-Article Comparison
Yujian Liu Xinliang Frederick Zhang Kaijian Zou Ruihong Huang Nick Beauchamp Lu Wang
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.