Patrick J. Chester

Patrick J. Chester

Phd Candidate (ABD)

New York University

Biography

Welcome! I am a Ph.D. candidate at New York University’s Politics Department. My substantive specialization is on the comparative politics of authoritarian regimes with a focus on Chinese politics. My methodological focus lies in applying text as data and machine learning tools towards understanding the behavior of regimes, media, and their citizenry.

In my first dissertation paper, I test whether Chinese state media engages in a strategy of “negative legitimation” by portraying the politics of liberal democracies as being chaotic. Using a novel method of measuring propaganda, I find robust evidence that is consistent with a negative legitimation strategy. My second dissertation paper expands on this result, finding that China’s state media targets executive elections in liberal democracies for unfavorable coverage.

One challenge I faced in my other dissertation projects was identifying Chinese-language semantically-related dictionaries. I address this need in my third dissertation project, where I present conclust, an algorithm based on word embeddings, that enables researchers to derive semantically related keywords using only 3-5 seed words. In addition to this paper, I have contributed to making neural network machine learning models accessible to a broader audience by writing functions for the quanteda.classifiers package.

Download my resumé.

Interests
  • Chinese politics
  • Computational social science
  • Comparative politics
  • Propaganda and framing
  • Text as data
  • Word embeddings
  • Web scraping
Education
  • Ph.D. in Politics (ABD), 2022 (est)

    New York University

  • Master of Pacific International Affairs, 2014

    School of Global Policy and Strategy at University of California: San Diego

  • Bachelor of the Arts in Political Science, 2009

    University of Minnesota: Morris

Working Papers

“Deligitimizing Election Coverage: Identifying a Negative Legitimation Strategy in China’s Coverage of Foreign Elections”

“Embedded Lexica: Extracting Topical Dictionaries from Unlabeled Corpora using Word Embeddings”

“Vaccine Nationalism: Propaganda and China’s Media Coverage of Vaccines”
with Victor Shih

“Informational Statecraft and Diaspora Mobilization”
with Audrye Wong

“Predicting Left-Right Positions from Hand-Coded Content Analysis using Machine Learning”
with Kenneth Benoit, Michael Laver, and Stefan Muller

“Extracting Measurements of De Jure Power from Constitutional Text”

“Networks of Power: Extracting Measurements of De Jure Power from Constitutional Text”

“Over-fishing, Conflict, and the South China Sea”
with Junjie Zhang

Teaching Experience

 
 
 
 
 
Teaching Assistant for Introduction to Data Science Course
Feb 2021 – Present New York
Head TA for course introducing undergraduates to supervised machine learning using Python.
 
 
 
 
 
Teaching Assistant for American Politics Course
Sep 2020 – Dec 2020 New York

Responsibilities include:

  • Instructing students
  • Generating assignment and exam reports using Rmarkdown
 
 
 
 
 
Teaching Assistant for Comparative Politics Course
Sep 2019 – Dec 2019 New York
Taught students core concepts and theories associated with the the Comparative Politics subfield.
 
 
 
 
 
Teaching Assistant for International Politics Course
Feb 2018 – May 2018 New York
Instructed undergraduates in the state of academic research in the field of International Politics.
 
 
 
 
 
Teaching Assistant for Research Methods Course
Sep 2016 – Dec 2016 New York
  • Worked as head TA to prepare course materials and collaborate with other Teaching Assistants.
  • Taught undergraduate students basics of regression analysis, causal identification, and Stata.
 
 
 
 
 
Teaching Assistant for Text as Data course
Feb 2016 – May 2016 New York
Instructed graduate students how to perform supervised and unsupervised machine learning using R.

Skills

R

-2

Python
Mandarin Chinese

Contact