Daniel Weitzel


University of Vienna


Welcome to my website. I am a post doctoral researcher at the Department of Government at the University of Vienna. I received my Ph.D. in Government from the Department of Government at the University of Texas at Austin in 2020.

My research interests focus on comparative politics and quantitative and computational methods. I use text as data, Bayesian statistics, and machine learning to study the interaction of political parties and voters. In my dissertation I examine the determinants and consequences of negative campaigning in multi-party electoral competition in Europe. I am also interested in estimating the prevalence and consequences of variation of political knowledge and information in the mass public and media.

I have received an M.Sc. in Statistics from the Department of Statistics and Data Science at the University of Texas at Austin. Before coming to UT Austin I received an M.Sc. in Political Behaviour from the Department of Government of the University of Essex/England and a B.A. and M.A. in Political Science and Sociology from the Institute of Political Science at the Friedrich-Alexander University Erlangen-Nuremberg/Germany.


  • Political Parties
  • Elections and Voting Behavior
  • Bayesian Statistics
  • Natural Language Processing
  • Machine Learning


  • 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

Leadership Turnovers and Their Electoral Consequences

In this paper, we use a novel dataset that covers nine advanced democracies between the early 1990s and 2019 to test whether the …

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.)

“Democracy: Global, Historical Measures Based on Observables” (with John Gerring, Dan Pemstein, Svend-Erik Skaaning).

Political Parties and Elections

“Curvilinearity in Political Parties: Evidence from two SPD membership referenda” (with Kai Jäger)

“The Non-Democratic Consequences of Intra-Party Democracy: Evidence from the SPD membership referendum” (with Kai Jäger)

Voter Behavior and Knowledge

“French Political Knowledge” (Robert Luskin, Bruno Cautres, Sherry Lowrance)

“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)