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News sources undergo the process of selecting newsworthy information whencovering a certain topic. The process inevitably exhibits selection biases,i.e. news sources' typical patterns of choosing what information to include innews coverage, due to their agenda differences. To understand the magnitude andimplications of selection biases, one must first discover (1) on what topics dosources typically have diverging definitions of "newsworthy" information, and(2) do the content selection patterns correlate with certain attributes of thenews sources, e.g. ideological leaning, etc.  The goal of the paper is to investigate and discuss the challenges ofbuilding scalable NLP systems for discovering patterns of media selectionbiases directly from news content in massive-scale news corpora, withoutrelying on labeled data. To facilitate research in this domain, we propose andstudy a conceptual framework, where we compare how sources typically mentioncertain controversial entities, and use such as indicators for the sources'content selection preferences. We empirically show the capabilities of theframework through a case study on NELA-2020, a corpus of 1.8M news articles inEnglish from 519 news sources worldwide. We demonstrate an unsupervisedrepresentation learning method to capture the selection preferences for howsources typically mention controversial entities. Our experiments show thatthat distributional divergence of such representations, when studiedcollectively across entities and news sources, serve as good indicators for anindividual source's ideological leaning. We hope our findings will provideinsights for future research on media selection biases.

Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start