Learning to Predict US Policy Change Using New York Times Corpus with Pre-Trained Language Model

With the process of economic globalization and political multi-polarization accelerating, it is especially important to predict policy change in the United States. While current research has not taken advantage of the rapid advancement in the natural language processing and the relationship between news media and policy change, we propose a BERT-based model to predict policy change in the United States, using news published by the New York Times. Specifically, we propose a large-scale news corpus from the New York Times covers the period from 2006 to 2018. Then we use the corpus to fine-tune the pre-trained BERT language model to determine whether the news is on the front page, which corresponds to the policy priority. We propose a BERT-based Policy Change Index (BPCI) for the United States to predict the policy change in the future short period of time. Experimental results in the New York Times Corpus demonstrate the validity of the proposed method.