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.

翻译：新闻来源在报道特定话题时会经历选择具有新闻价值信息的过程。由于议程差异，这一过程不可避免地表现出选择偏见，即新闻来源在新闻覆盖中选择包含何种信息的典型模式。为理解选择偏见的程度及其影响，首先需发现：(1) 来源在哪些话题上通常对“具有新闻价值”信息的定义存在分歧；(2) 内容选择模式是否与新闻来源的某些属性（如意识形态倾向等）相关。本文旨在探讨并研究构建可扩展自然语言处理系统时面临的挑战，这类系统需直接从大规模新闻语料库中挖掘媒体选择偏见模式，且不依赖标注数据。为促进该领域研究，我们提出并研究了一个概念框架，通过比较来源如何典型性地提及某些争议实体，并将其作为来源内容选择偏好的指标。我们通过一个案例研究——基于包含来自全球519家新闻来源的180万篇英文新闻文章的NELA-2020语料库——实证展示了该框架的能力。我们采用无监督表示学习方法，捕捉来源典型提及争议实体时的选择偏好。实验表明，当跨实体和新闻来源集体研究时，此类表示的分布差异可作为个体来源意识形态倾向的有效指标。希望我们的发现能为未来媒体选择偏见研究提供见解。