• Advisor: Dr. Lu Wang
• Duration: May 2021 - Jan 2022
• Publication venue: Findings of NAACL 2022
• Summary: 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 experiments. Our model POLITICS outperforms strong baselines on 8 out of 11 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.