We explore the task of predicting the leading political ideology or bias of
news articles. First, we collect and release a large dataset of 34,737 articles
that were manually annotated for political ideology -left, center, or right-,
which is well-balanced across both topics and media. We further use a
challenging experimental setup where the test examples come from media that
were not seen during training, which prevents the model from learning to detect
the source of the target news article instead of predicting its political
ideology. From a modeling perspective, we propose an adversarial media
adaptation, as well as a specially adapted triplet loss. We further add
background information about the source, and we show that it is quite helpful
for improving article-level prediction. Our experimental results show very
sizable improvements over using state-of-the-art pre-trained Transformers in
this challenging setup.

本文探讨新闻文章中的政治意识形态或偏见的预测任务，提出了一个具有挑战性的实验设置并收集了大量的数据集，在建模方面使用了敌对媒体适应和三元组损失等策略，并在文章级别上加入了源背景信息，实验结果表明这种方法在这种具有挑战性的情况下比使用先进的预先训练转换器有相当大的改进。