I am a political scientist and data scientist.

I study why and how people lie in the social media era.

In my dissertation Lies to Friends, Truths to Strangers: Anonymity, Preference Falsification, and Opinion Polarization in Authoritarian China, I develop a new theory to explain why people lie about their political preferences online and offline in the social media era. I argue that the new pattern of preference falsification unintendedly consolidates dictatorships. Using natural language processing, machine learning, and causal inference methods, I show the behavioral pattern and its implications with an original dataset of online political discourse and networks among over 1 million Chinese social media users.

Broadly defined, my interests lie at the intersection of political behavior, political economy, and big-data methods. I apply machine learning models to text, network, and survey data to discover and explain political phenomena.

Political Behavior and Data Science

Bail, Christopher A., Lisa P. Argyle, Taylor W. Brown, John P. Bumpus, Haohan Chen, M.B. Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. 2018. “Exposure to Opposing Views on Social Media Can Increase Political Polarization.” Proceedings of the National Academy of Sciences, vol. 115, no. 37: 9216–9221. Link. PDF. Media: NYTimes, Washington Post, LATimes, BBC

Political Economy

Chen, Haohan, and Herbert Kitschelt. Under review. “Political Linkage Strategies and Social Investment Policies: Clientelism and Educational Policy in the Developing World.” Written for The World Politics of Social Investment, edited by Bruno Palier and Silja Haeusermann. Oxford: Oxford University Press.
Appendix: An online app to visualize and model global education and clientelism data

Chen, Haohan. 2017. “Why the Poor Tolerate Inequality in Developing Democracies: Weak States and Clientelism.” Awarded Annual Best Prelim Exam Paper in Political Science, Duke University, 2017. Interative slides

Chen, Haohan, and Zeren Li. 2018. “Promoting Competent or Loyal Officials? Modeling Political Networks in China with the AME Model.”

Quantitative Methods / Data Science

Aldrich, John H., Haohan Chen, Victoria Dounoucos, Joshua Lerner, Pedro Magahaes, and Greg Schober. Under Review. “Institutional Influences on Behavior and Selection Effects.”

Zhang, Ruodan, Haohan Chen, and Jill Nicholson-Crotty. 2018. “Does government funding to public charities crowd out or crowd in volunteers?” Presented at ARNOVA 2018, Austin, TX.

Chen, Haohan, and Yanlan Liu. 2018. “Climate, Governance, and Disease: Environmental and Institutional Determinants of Malaria Spread in Africa.” Report. Code.

Political Economy

Business, Politics, and Economic Growth (Spring 2017) Syllabus
Philosophy, Politics, and Economics (Fall 2016)

Quantitative Methods / Data Science

Summer Institute in Computational Social Science (Summer 2018) Program Website
Probability and Regression (Fall 2018) Handouts and Code
Advanced Regression (Spring 2018) Handouts and Code
Probability (Fall 2017)
Math Camp (Summers 2014, 2015) LaTeX Tutorial

Mind Maps of Political Economy

Below are mind maps summarizing some important topics of political economy. I drew them when I reviewed for my qualifying exam in 2015.

Preference Formation
Market, Growth, and Development
Electoral Politics
Political Economy of Geography
Microeconomics: Trade-off and Behavioral Economics

Notes of Data Science Courses

Below are my favorite online data science courses, with links to my notes (mostly instructors’ slides and handouts with my annotation).

Natural Language Processing by Dragomir Radev (Coursera) Notes
deeplearning.ai sequence by Andrew Ng (Coursera) Notes
Advanced Machine Learning sequence by HSE (Coursera) Notes
Deep Learning for NLP (Stanford) Notes
Spatial-temporal Model by Colin Rundel (Duke) Notes

• haohan.chen@duke.edu
• 140 Science Drive, 208 Gross Hall, Campus Box 90204, Durham, NC 27708, USA