  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.
