Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
Please log in

Reading China: Predicting policy change with machine learning

Weifeng Zhong (Mercatus Center at George Mason University)
17:2518:05 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Average rating: ****.
(4.75, 4 ratings)

Who is this presentation for?

  • Those interested in the Chinese economy, novel applications of machine learning, or both



Prerequisite knowledge

  • Basic knowledge of neural networks and natural language processing

What you'll learn

  • Explore the first index to predict China's "next big things"
  • Learn a new way to uncover hidden patterns from trivial labels and the wide range of potential applications it enables


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.

Photo of Weifeng Zhong

Weifeng Zhong

Mercatus Center at George Mason University

Weifeng Zhong is a senior research fellow at the Mercatus Center at George Mason University. His work focuses on bridging the field of natural language processing and machine learning to economic policy studies. His other research interests include the political economy, US-China economic relations, and China’s economic issues. Weifeng is a core maintainer of the open source Policy Change Index (PCI) project, a framework that uses machine learning to “read” large volumes of text and detect subtle, structural changes embedded in it. As a first use case, the PCI for China is an algorithm that can predict China’s policy changes using the information in the government’s official newspaper. The PCI framework has received significant academic interest and media coverage. The resources of this project are freely available at Weifeng has been published in a variety of scholarly journals, including the Journal of Institutional and Theoretical Economics. His research and writings have been featured in the Financial Times, Foreign Affairs, The National Interest, Real Clear Markets, Real Clear Politics, the South China Morning Post, and the Wall Street Journal, among others.

Comments on this page are now closed.


Picture of Weifeng Zhong
5/05/2019 5:33 BST

Thanks for coming to my talk, Lorenzo. The slides the talk, and those from our other talks, can be found here:

3/05/2019 11:53 BST

Are the slides of the talk available somewhere?