We propose an interpretable model to score the bias present in web documents,
based only on their textual content. Our model incorporates assumptions
reminiscent of the Bradley-Terry axioms and is trained on pairs of revisions of
the same Wikipedia article, where one version is more biased than the other.
While prior approaches based on absolute bias classification have struggled to
obtain a high accuracy for the task, we are able to develop a useful model for
scoring bias by learning to perform pairwise comparisons of bias accurately. We
show that we can interpret the parameters of the trained model to discover the
words most indicative of bias. We also apply our model in three different
settings - studying the temporal evolution of bias in Wikipedia articles,
comparing news sources based on bias, and scoring bias in law amendments. In
each case, we demonstrate that the outputs of the model can be explained and
validated, even for the two domains that are outside the training-data domain.
We also use the model to compare the general level of bias between domains,
where we see that legal texts are the least biased and news media are the most
biased, with Wikipedia articles in between. Given its high performance,
simplicity, interpretability, and wide applicability, we hope the model will be
useful for a large community, including Wikipedia and news editors, political
and social scientists, and the general public.

本文介绍了一个基于 Wikipedia 文章的可解释模型来评分网络文档的偏见性，并且应用该模型研究了 Wikipedia 文章中的偏见的时间演变，新闻媒体的偏见比较和法律修正案中的偏见评分，最后发现在各个领域中，法规文件是最少有偏见的，新闻媒体是最有偏见的，Wikipedia 文章则居中。

所以说，解释性模型在文档评分偏差中的应用

It's All Relative: Interpretable Models for Scoring Bias in Documents

In this paper, we analyze content sharing between news sources in the
alternative and mainstream media using a dataset of 713K articles and 194
sources. We find that content sharing happens in tightly formed communities,
and these communities represent relatively homogeneous portions of the media
landscape. Through a mix-method analysis, we find several primary content
sharing behaviors. First, we find that the vast majority of shared articles are
only shared with similar news sources (i.e. same community). Second, we find
that despite these echo-chambers of sharing, specific sources, such as The
Drudge Report, mix content from both mainstream and conspiracy communities.
Third, we show that while these differing communities do not always share news
articles, they do report on the same events, but often with competing and
counter-narratives. Overall, we find that the news is homogeneous within
communities and diverse in between, creating different spirals of sameness.

本研究基于共 713,000 篇文章和 194 个来源的数据，分析了另类和主流媒体之间的内容共享。研究发现，这种内容共享发生在紧密形成的社区中，并且这些社区代表着媒体格局的相对同质性部分。尽管存在内容沉淀现象，特定来源如 The Drudge Report 混合来自主流和阴谋社区的内容。同时，虽然这些不同社区不总是分享新闻文章，但他们常常以竞争性和相互矛盾的叙事方式报道同一事件。总体而言，我们发现新闻在社区内是同质性的，在社区间则是多样的，营造着不同的同质化循环。