Using machine learning to detect shifts in government policy

The Data Exchange Podcast: Weifeng Zhong on bringing NLP and machine learning to economic policy studies.

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In this episode of the Data Exchange I speak with Weifeng Zhong, Senior Research Fellow at the Mercatus Center at George Mason University. He is the core maintainer of the open source Policy Change Index (PCI), a framework that uses machine learning and NLP to “process and read” large amounts of text to discern government priorities and policies. The initial PCI is focused on major policy shifts in China and uses NLP and machine learning to process and analyze  the People’s Daily.

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More broadly, Weifeng’s work focuses on bringing NLP and machine learning to economic policy studies. PCI is a great example of potential applications of ML and NLP to many disciplines in the social science and humanities. As Weifeng pointed out in our conversation, tools now make it possible for non experts to automate text processing and understanding and allow scholars to more efficiently study large corpuses.

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[Image: National Library of China from Wikimedia]