Florian S. Schaffner
Postdoctoral Researcher
Department of Political Science
University of Zurich
About Me
I am a postdoctoral researcher at the Department of Political Science at the University of Zurich. My research focuses on political behavior, political psychology, affective polarization, representation, and direct democracy. I am also interested in methodological issues in quantitative political science, such as machine learning and language models. I received my PhD from the University of Oxford, where I examined the effects of empathy on political behavior.
Education
DPhil in Politics
University of Oxford, 2024
MSc in Comparative Politics
London School of Economics and Political Science, 2018
BA in Social Sciences
University of Zurich, 2016
Publications
Populism and governmentalism as thin-centered ideologies: Emotions and frames on social media (with Giuliano Formisano, Jörg Friedrichs, and Niklas Stoehr). European Journal of Political Research. First View, 1-20. 2025. Abstract | PDF
No existing model of political rhetoric fully captures the complex interplay between the mainstream-populism divide and appealing to emotions like fear and anger. We present a new conceptualization and procedure that defines populism in relation to governmentalism, operationalizes both through communication frames, and allows for the analysis of emotions. We separate governmentalist-populist contestation from contestation between government and opposition, solving a longstanding theoretical and empirical problem. Analyzing one million tweets by politicians and their audiences, we fine-tune and employ supervised machine learning (transformer models) to classify populist and governmentalist communication. We find that populist tweets appeal more to anger and more to fear than governmentalist tweets. While we deploy our approach for tweets about Coronavirus in the UK, the procedure is transferable to other contexts and communication platforms.
Moaners, Gloaters, and Bystanders: Perceived Fairness of the United Kingdom’s 2016 Referendum on the European Union. Political Studies. 69(2), 278–306. 2020. Abstract | PDF
Referendums divide the electorate into winners, losers, and abstainers. Research has shown that these three groups tend to differ substantially in their evaluations of the fairness of a referendum. However, no study has investigated the nature and determinants of citizens’ perceptions of the fairness of a national referendum from long before until long after the vote. I address this lacuna by studying perceived fairness of the Brexit referendum using a four-wave panel dataset that tracks perceptions of fairness from before the referendum to 10 months after. The results demonstrate that winners, losers, and abstainers differ significantly in their fairness expectations and fairness evaluations after the vote and that the gap between them widened over time. Strength of identification with the referendum camps substantially moderates perceived fairness. Winners who expected to win did not expect the referendum to be conducted more fairly than winners who expected to lose.
Working Papers
Comparing the Performance of Machine Learning Ensembles for Multilevel Regression and Poststratification Models (with Philipp Broniecki, Lucas Leemann, and Reto Wüest)
Empathy and Affective Polarization After the Brexit Referendum
Parties and the People: Why Do Parties Support Direct Democracy? (with Sarah Engler and Lucas Leemann)
Teaching
Direkte Demokratie in globaler Perspektive (Undergraduate), Department of Political Science, University of Zurich, Spring Semester 2026
Natural Language Processing for Political Science (Postgraduate), Department of Political Science, University of Zurich, Spring Semester 2026
Master’s Thesis Colloquium (Postgraduate), Department of Political Science, University of Zurich, Fall Semester 2025
Natural Language Processing for Political Science (Postgraduate), Department of Political Science, University of Zurich, Spring Semester 2025
Master’s Thesis Colloquium (Postgraduate), Department of Political Science, University of Zurich, Spring Semester 2025
Political Systems and Theories I, Lectures (Undergraduate), Department of Political Science, University of Zurich, Fall Semester 2024
Preparation for the Research Seminar Political Data Journalism (Postgraduate), Department of Political Science, University of Zurich, Fall Semester 2024
Political Systems and Theories II, Tutorials (Undergraduate), Department of Political Science, University of Zurich, Spring Semester 2024
Quantitative Text Analysis (Postgraduate), Department of Political Science, University of Zurich, Spring Semester 2024
Political Systems and Theories I, Tutorials (Undergraduate), Department of Political Science, University of Zurich, Fall Semester 2023
Political Systems and Theories II, Tutorials (Undergraduate), Department of Political Science, University of Zurich, Spring Semester 2023
Quantitative Text Analysis (Postgraduate), Department of Political Science, University of Zurich, Spring Semester 2023
Political Systems and Theories I, Tutorials (Undergraduate), Department of Political Science, University of Zurich, Fall Semester 2022
Intermediate Statistics (Postgraduate), Department of Politics and International Relations, University of Oxford, Michaelmas Term 2021
Intermediate Statistics (Postgraduate), Department of Politics and International Relations, University of Oxford, Michaelmas Term 2020
Thesis Supervision (Undergraduate), Wadham College, University of Oxford, Trinity Term 2020
Introduction to Statistics (Postgraduate), Faculty of Law, University of Oxford, Michaelmas Term 2019
Software
autoMrP: Improving MrP with Ensemble Learning. R package, available on CRAN
bodleianlibraries: Search the Bodleian Libraries Catalogue (SOLO) directly from the R console. R package, available on GitHub
websearchr: Access Domains and Search Popular Websites. R package, available on CRAN
xaringanbeamer: Beamer theme for the xaringan package. R package, available on GitHub
Xplorer: Convenient Data Exploration. R package, available on GitHub
Contact
Dr. Florian Schaffner
Department of Political Science
University of Zurich
Affolternstrasse 56
8050 Zurich
Switzerland
Office: AFL-H-304
E-mail: schaffner@ipz.uzh.ch