January 7, 2020

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Reading China: Predicting policy change with machine learning

Reading China: Predicting policy change with machine learning

Weifeng Zhong shares a machine learning algorithm built to read the Peoples Daily (the official newspaper of the Communist Party of China) and predict changes in Chinas policy priorities. The output of this algorithm, named the Policy Change Index (PCI) of China, turns out to be a leading indicator of the actual policy changes in China since 1951.
Talk Title Reading China: Predicting policy change with machine learning
Speakers Weifeng Zhong (Mercatus Center at George Mason University)
Conference Strata Data Conference
Conf Tag Making Data Work
Location London, United Kingdom
Date April 30-May 2, 2019
URL Talk Page
Slides Talk Slides
Video

Weifeng Zhong shares a machine learning algorithm built to “read” the People’s Daily (the official newspaper of the Communist Party of China) and predict changes in China’s policy priorities. The output of this algorithm, named the Policy Change Index (PCI) of China, turns out to be a leading indicator of the actual policy changes in China since 1951. The PCI is designed from two building blocks: the full text of the People’s Daily as input data and a set of machine learning techniques to detect changes in how this newspaper prioritizes policy issues. Due to the unique role of the People’s Daily in China’s propaganda system, detecting changes in this newspaper can predict changes in China’s policies. The construction of the PCI doesn’t require the researcher’s understanding of the Chinese context, which suggests a wide range of applications in other settings, such as predicting changes in other (ex-)communist regimes’ policies, measuring decentralization in central-local government relations, quantifying media bias in democratic countries, and predicting changes in lawmaker’s voting behavior and in judges’ ideological leanings.

paper algorithm government machine learning
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