Quantification of the political leaning of online news articles can aid in
understanding the dynamics of political ideology in social groups and measures
to mitigating them. However, predicting the accurate political leaning of a
news article with machine learning models is a challenging task. This is due to
(i) the political ideology of a news article is defined by several factors, and
(ii) the innate nature of existing learning models to be biased with the
political bias of the news publisher during the model training. There is only a
limited number of methods to study the political leaning of news articles which
also do not consider the algorithmic political bias which lowers the
generalization of machine learning models to predict the political leaning of
news articles published by any new news publishers. In this work, we propose a
knowledge-infused deep learning model that utilizes relatively reliable
external data resources to learn unbiased representations of news articles
using their global and local contexts. We evaluate the proposed model by
setting the data in such a way that news domains or news publishers in the test
set are completely unseen during the training phase. With this setup we show
that the proposed model mitigates algorithmic political bias and outperforms
baseline methods to predict the political leaning of news articles with up to
73% accuracy.

利用可靠的外部数据资源学习新闻文章的公正表示，提出了一种知识注入的深度学习模型，旨在预测新闻文章的政治倾向，解决了现有学习模型在模型训练中受到新闻发布者的政治偏见影响的问题，从而消除了算法政治偏见，并在准确性方面优于基线方法，可达到 73% 的准确率。

学习无偏见的新闻文章表征：一种融合知识的方法

Learning Unbiased News Article Representations: A Knowledge-Infused  Approach

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

The profusion of online news articles makes it difficult to find interesting
articles, a problem that can be assuaged by using a recommender system to bring
the most relevant news stories to readers. However, news recommendation is
challenging because the most relevant articles are often new content seen by
few users. In addition, they are subject to trends and preference changes over
time, and in many cases we do not have sufficient information to profile the
reader.
In this paper, we introduce a class of news recommendation systems based on
context trees. They can provide high-quality news recommendation to anonymous
visitors based on present browsing behaviour. We show that context-tree
recommender systems provide good prediction accuracy and recommendation
novelty, and they are sufficiently flexible to capture the unique properties of
news articles.

本文介绍了一种基于上下文树的新闻推荐系统，能够根据用户的浏览行为为匿名访客提供高质量、准确、新颖的新闻推荐。该系统能够很好地捕捉新闻文章的独特性质，并提供良好的预测准确性。