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。