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

Ramy Baly, Giovanni Da San Martino, James Glass, Preslav Nakov

Machine Learning for NLP Long Paper

Gather-3C: Nov 17, Gather-3C: Nov 17 (18:00-20:00 UTC) [Join Gather Meeting]

You can open the pre-recorded video in a separate window.

Abstract: 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.
NOTE: Video may display a random order of authors. Correct author list is at the top of this page.

Connected Papers in EMNLP2020

Similar Papers