POLITICS: pretraining with same-story article comparison for ideology prediction and stance detection

Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nick Beauchamp, Lu Wang
arXiv preprint arXiv:2205.00619
May 2, 2022


Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.

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