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

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Abstract

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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under 61775175, 61771378 and 61601355, and by the Key Research and Development Program of Shaanxi Province - Key Industry Innovation Chain (Group) - Industrial Field under No.2019ZDLGY10-06.

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, China

    Guoshuai Zhang, Jiaji Wu & Qingyu Cheng

  2. Xi’an Brain Perception Technology Development Co., Ltd, Xi’an, China

    Jiaji Wu, Mingzhou Tan & Zhongjie Yang

  3. School of Artificial Intelligence, Xidian University, Xi’an, China

    Mingzhou Tan, Zhongjie Yang & Hong Han

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  1. Guoshuai Zhang
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  2. Jiaji Wu
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  3. Mingzhou Tan
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  4. Zhongjie Yang
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  6. Hong Han
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Correspondence to Jiaji Wu.

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Zhang, G., Wu, J., Tan, M. et al. Learning to Predict U.S. Policy Change Using New York Times Corpus with Pre-Trained Language Model. Multimed Tools Appl 79, 34227–34240 (2020). https://doi.org/10.1007/s11042-020-08946-y

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