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

Code

• github.com/launchnlp/politics
  OfficialIn paperpytorch★ 39
• github.com/lyh6560new/p3sum
  pytorch★ 10

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.

Tasks

Reproductions