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arXiv · 2205.00619

POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

Yujian Liu·Xinliang Frederick Zhang·David Wegsman·Nick Beauchamp·Lu Wang
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Abstract

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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Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nick Beauchamp, Lu Wang. 2022-05-02. POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection. https://arxiv.org/abs/2205.00619

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