Daniel Weitzel

Assistant Professor

Colorado State University


Welcome to my website. I am an assistant professor in the Department of Political Science and faculty affiliate at the Data Science Research Institute at Colorado State University. I am also a Senior Research Fellow in the Department of Government at the University of Vienna. I received my Ph.D. in Government from the University of Texas at Austin in 2020 and was a postdoctoral researcher at the University of Vienna between 2020-22.

I use text as data and machine learning to study democracy, democratic backsliding, negative campaigning, and the strategic behavior of political actors in elections, particularly the interaction between political parties and voters in democracies. I am also interested in estimating the prevalence and consequences of variation of political knowledge and information in the mass public and media.


  • Democratization and Backsliding
  • Elections and Voting Behavior
  • Political Parties
  • Natural Language Processing
  • Machine Learning
  • Survey Design and Methodology


  • Ph.D. Government, 2020

    University of Texas

  • M.Sc. Statistics, 2019

    University of Texas

  • M.Sc. Political Behaviour, 2014

    University of Essex

  • M.A. Political Science, 2013

    University of Erlangen-Nuremberg

  • B.A. Political Science and Sociology, 2011

    University of Erlangen-Nuremberg

Under Review

Extreme Recall: Which Politicians Come to Mind?

How do people understand parties? Using data from two original surveys fielded nearly a decade apart, we shed light on people’s mental …

A Measurement Gap? Effect of Survey Instrument and Scoring on the Partisan Knowledge Gap

Research suggests that partisan gaps in political knowledge with partisan implications are wide and widespread. Using a series of …

Valence Attacks in Multi-Party Elections

How do political parties attack each other in multi-party systems? While voters in multi-party systems respond to attacks on the …

Electoral Contestation: A New Measure and an Empirical Test

The study of electoral competition generally focuses on districts or regions (rather than polities) or a narrow slice of democratic …


Natural Language Processing and Machine Learning

“Not News: Provision of Apolitical News in the U.S. News Media” (with Suriyan Laohaprapanon and Gaurav Sood). Working paper is available here, preliminary analysis based on over 5k cable news broadcasts can be found on Github.)

Voter Behavior and Knowledge

“Hidden Knowledge, Veiled Ignorance: Do People Know More – or Even Less – about Politics than Commonly Thought?” (with Robert C. Luskin, and Gaurav Sood)

“Misinformation about Misinformation? Of Headlines and Survey Design” (with Robert C. Luskin, Gaurav Sood, and Yul Min Park, early working paper)

Recent Posts

An Analysis of Global and U.S. democracy

I gave a one hour presentation at the 5th annual international symposium at Colorado State University. The topic of my talk was a …

A Quick Intro to R and Democracy Data

I recently hosted a workshop introducing students at Colorado State University to R, the tidyverse, and the V-Dem data set. We had a …

How to install R on Ubuntu

In this post I'll explain how to quickly install R on Ubuntu-based operating systems and make sure that R packages are installed with …

A Guide to Linux for Social Scientists

When I was a graduate student, I immensely benefitted from a friend's guide to Linux for social scientists. Unfortunately, that post is …

Error message org.freedesktop.Platform 20.08

Updating packages on my Ubuntu based Pop OS operating system recently I got an error message about the end of life of …