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

We Can Detect Your Bias: Predicting the Political Ideology of News Articles