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.

翻译：公众舆论深受新闻媒体所提供信息的影响，而这类信息本身又可能受到媒体机构意识形态偏好的塑造。尽管学界已大量关注媒体通过显性意识形态措辞或议题选择所展现的偏见，但媒体影响舆论的另一种更为隐蔽的方式，在于策略性地纳入或省略可能支持某一方的党派事件。我们开发了一个基于潜变量的框架，通过比较同一新闻事件的多篇报道，识别出那些因纳入或省略而揭示意识形态倾向的党派事件。实验首先验证了党派事件选择现象的存在，进而表明：相较于竞争基线方法，文本对齐与跨文档比较能更有效地检测党派事件及文章意识形态。研究结果揭示了媒体偏见的高级表现形式——即使在恪守客观与无党派准则的主流媒体中，这种偏见也普遍存在。我们的代码库和数据集已发布于 https://github.com/launchnlp/ATC。