Search bias analysis is getting more attention in recent years since search
results could affect In this work, we aim to establish an automated model for
evaluating ideological bias in online news articles. The dataset is composed of
news articles in search results as well as the newspaper articles. The current
automated model results show that model capability is not sufficient to be
exploited for annotating the documents automatically, thereby computing bias in
search results.

本文旨在建立一种自动模型，评估在线新闻文章的意识形态偏见，结果表明目前的自动模型能力不足以被用于自动注释文档，进而计算搜索结果中的偏见。

新闻文章中的政治偏见量化

Quantifying Political Bias in News Articles

We propose a 'legal approach' to hate speech detection by operationalization
of the decision as to whether a post is subject to criminal law into an NLP
task. Comparing existing regulatory regimes for hate speech, we base our
investigation on the European Union's framework as it provides a widely
applicable legal minimum standard. Accurately judging whether a post is
punishable or not usually requires legal training. We show that, by breaking
the legal assessment down into a series of simpler sub-decisions, even
laypersons can annotate consistently. Based on a newly annotated dataset, our
experiments show that directly learning an automated model of punishable
content is challenging. However, learning the two sub-tasks of `target group'
and `targeting conduct' instead of an end-to-end approach to punishability
yields better results. Overall, our method also provides decisions that are
more transparent than those of end-to-end models, which is a crucial point in
legal decision-making.

本文提出通过将判断众声喧哗是否应受刑事法律管辖作为 NLP 任务的 ' 法律方法 ' 来检测众声喧哗。基于欧盟法律框架和建立一个新的数据集，作者将众声喧哗检测分解成两个子任务：' 目标群体 ' 和' 针对行为 '，并且通过简单化任务的方式使普通人能够一致地帮助对数据集进行注释。作者的实验表明，直接学习可惩罚内容的自动化模型是具有挑战性的。相比之下，学习两个子任务而不是端到端的方式可能更好，因为它提供的决策比端到端模型更加透明，这在法律决策中至关重要。