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.

本篇论文探讨了如何建立可扩展 NLP 系统来直接从海量新闻语料库中发现媒体选择偏差模式的挑战，并针对新闻源的意识形态进行了实证研究，通过无监督的学习方法来捕捉新闻语料库中的媒体选择倾向，为未来的媒体选择偏差研究提供了启示。

通过比较实体相关信息来源，实现新闻覆盖中选择偏差的语料库规模发现

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

This paper develops a novel unsupervised algorithm for belief representation
learning in polarized networks that (i) uncovers the latent dimensions of the
underlying belief space and (ii) jointly embeds users and content items (that
they interact with) into that space in a manner that facilitates a number of
downstream tasks, such as stance detection, stance prediction, and ideology
mapping. Inspired by total correlation in information theory, we propose the
Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to
project both users and content items (e.g., posts that represent user views)
into an appropriate disentangled latent space. To better disentangle latent
variables in that space, we develop a total correlation regularization module,
a Proportional-Integral (PI) control module, and adopt rectified Gaussian
distribution to ensure the orthogonality. The latent representation of users
and content can then be used to quantify their ideological leaning and
detect/predict their stances on issues. We evaluate the performance of the
proposed InfoVGAE on three real-world datasets, of which two are collected from
Twitter and one from U.S. Congress voting records. The evaluation results show
that our model outperforms state-of-the-art unsupervised models by reducing
10.5% user clustering errors and achieving 12.1% higher F1 scores for stance
separation of content items. In addition, InfoVGAE produces a comparable result
with supervised models. We also discuss its performance on stance prediction
and user ranking within ideological groups.

本论文提出了一个新颖的针对极化网络的无监督算法，能够识别潜在维度、将用户和内容项目结合到一个合适的解缠结的潜在空间中，并且能够定量权衡他们在意识形态上的倾向，探究他们在问题上的立场，是一个在用户聚类错误降低 10.5％，提高 12.1％的 F1 得分，同时与监督模型产生可比较的结果的模型。