  News sources undergo the process of selecting newsworthy information when
covering a certain topic. The process inevitably exhibits selection biases,
i.e. news sources' typical patterns of choosing what information to include in
news coverage, due to their agenda differences. To understand the magnitude and
implications of selection biases, one must first discover (1) on what topics do
sources typically have diverging definitions of "newsworthy" information, and
(2) do the content selection patterns correlate with certain attributes of the
news sources, e.g. ideological leaning, etc.
  The goal of the paper is to investigate and discuss the challenges of
building scalable NLP systems for discovering patterns of media selection
biases directly from news content in massive-scale news corpora, without
relying on labeled data. To facilitate research in this domain, we propose and
study a conceptual framework, where we compare how sources typically mention
certain controversial entities, and use such as indicators for the sources'
content selection preferences. We empirically show the capabilities of the
framework through a case study on NELA-2020, a corpus of 1.8M news articles in
English from 519 news sources worldwide. We demonstrate an unsupervised
representation learning method to capture the selection preferences for how
sources typically mention controversial entities. Our experiments show that
that distributional divergence of such representations, when studied
collectively across entities and news sources, serve as good indicators for an
individual source's ideological leaning. We hope our findings will provide
insights for future research on media selection biases.
