I am an Assistant Professor (Lecturer) and Ad Astra Fellow in the School of Politics and International Relations at University College Dublin. Previously, I was a Senior Researcher in the Department of Political Science at the University of Zurich. I hold a PhD in Political Science from Trinity College Dublin.
My research focuses on political representation, party competition, political communication, and public opinion. I apply a range of research methods, such as quantitative text analysis, machine learning, speech recognition, and computer vision. My work has been published or is forthcoming in journals such as The Journal of Politics, Political Communication, Political Science Research and Methods, and the European Journal of Political Research, among others.
I am a founding member of the Connected_Politics Lab at University College Dublin and a non-resident fellow at the Digital Democracy Lab (University of Zurich). I am also a co-author of the quanteda R package for quantitative text analysis, member of the Quanteda Initiative, and maintainer of the Irish Polling Indicator.
Shaun Bowler, Gail McElroy, and Stefan Müller. Accepted for Publication. “Voter Expectations of Government Formation in Coalition Systems: The Importance of the Information Context.” European Journal of Political Research.
Stefan Müller. Accepted for Publication. “The Temporal Focus of Campaign Communication.” The Journal of Politics.
Stefan Müller. 2020. “Media Coverage of Campaign Promises Throughout the Electoral Cycle.” Political Communication 37(5): 696–718.
Stefan Müller and Tom Louwerse. 2020. “The Electoral Cycle Effect in Parliamentary Democracies.” Political Science Research and Methods 8(4): 795–802.
Shaun Bowler, Gail McElroy, and Stefan Müller. 2020. “Campaigns and the Selection of Policy-Seeking Representatives.” Legislative Studies Quarterly 45(3): 397–431.
Stefan Müller and Michael Jankowski. 2019. “Do Voters Really Prefer More Choice? Determinants of Support for Personalised Electoral Systems.” Journal of Elections, Public Opinion and Parties 29(2): 262–281.
Kenneth Benoit, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. 2018. “quanteda: An R Package for the Quantitative Analysis of Textual Data.” Journal of Open Source Software 3(30): 774.
Shaun Bowler, Gail McElroy, and Stefan Müller. 2018. “Voter Preferences and Party Loyalty under Cumulative Voting: Political Behaviour after Electoral Reform in Bremen and Hamburg.” Electoral Studies 51: 93–102.
Liam Kneafsey and Stefan Müller. 2018. “Assessing the Influence of Neutral Grounds on Match Outcomes.” International Journal of Performance Analysis in Sport 18(6): 892–905.
Kohei Watanabe and Stefan Müller. 2020. Quanteda Tutorials. https://tutorials.quanteda.io.
Social Media and Political Agenda Setting. Political Communication: revise & resubmit (with Fabrizio Gilardi, Theresa Gessler, and Maël Kubli).
Evidence for the Irrelevance of Irrelevant Events. Political Science Research and Methods: revise & resubmit (with Liam Kneafsey).
Moderators of the Incumbency Advantage: Evidence from Irish Local Elections, 1942–2019. Electoral Studies: revise & resubmit (with Michael Jankowski).
Social Media and Policy Responses to the COVID-19 Pandemic in Switzerland. Swiss Political Science Review: revise & resubmit (with Fabrizio Gilardi, Theresa Gessler, and Maël Kubli).
Gender, Candidate Emotional Expression, and Voter Reactions During Televised Debates (with Constantine Boussalis, Travis G. Coan, and Mirya R. Holman).
Building Research Infrastructures to Study Digital Technology and Politics: Lessons from Switzerland (with Fabrizio Gilardi, Lucien Baumgartner, Clau Dermont, Karsten Donnay, Theresa Gessler, Maël Kubli, and Lucas Leemann).
The Compass of Irish Politics is Moving to the Left (with Aidan Regan).
Explaining (In)Congruence Between Politicians’ Campaign Promises and Subsequent Legislative Priorities (with Naofumi Fujimura).
Causes and Electoral Effects of Nostalgic Rhetoric: A Cross-National Analysis of Party Communication (with Sven-Oliver Proksch).
Responding to Whom? Parties’ Issue Emphasis Strategies in a Dynamic Media Environment (with Fabrizio Gilardi, Theresa Gessler, and Maël Kubli).
Efficient and Scalable Analysis of Political Text (with Kenneth Benoit, Patrick Chester, and Michael Laver).
Do Candidates Tweet About Oirish Sheep? Examining the Irish #GE2020 Campaign on Social Media Using an Images-as-Data Approach (with Mark Belford, James P Cross, Derek Greene, and Martijn Schoonvelde).
If you would like to get access to the latest version of a paper, feel free to send me an e-mail.
2020 (Autumn): Introduction to Statistics. [Syllabus]
2021 (Spring): Connected_Politics. [Syllabus]
2019 (Autumn) & 2020 (Spring): Political Representation and Policy Preferences. [Syllabus]
2019 (Autumn): Quantitative Text Analysis. [Syllabus]
2019 (Spring): Quantitative Text Analysis. [Syllabus]
2020: Creating and Hosting an Academic Personal Website Using Hugo and GitHub, Connected_Politics Lab, University College Dublin (with Natalia Umansky).
2020: Quantitative Approaches to Linguistic and Textual Analysis and Data Visualisation using R, Scottish Graduate School of Social Science (with Theresa Gessler).
2020: Reproducible Research with Git and GitHub, Connected_Politics Lab, University College Dublin.
2020: Quantitative Text Analysis with quanteda, University of Bremen (with Kenneth Benoit).
2020: Quantitative Text Analysis for Absolute Beginners, COMPTEXT, Innsbruck (with Kenneth Benoit).
2020: Wrangling and Visualising Data Using R, Connected_Politics Lab, University College Dublin.
2019: Quantitative Text Analysis for Absolute Beginners, POLTEXT Pre-Conference Events, Tokyo.
2019: Introduction to Quantitative Text Analysis, Kobe University.
2019: An Introduction to Quantitative Text Analysis Using R and quanteda, Swiss National Science Foundation, Bern.
2019: Quantitative Text Analysis, University of Düsseldorf.
2019: An Introduction to Quantitative Text Analysis Using R and quanteda, University of Bergen.
2018: An Introduction to the Quanteda Package for Quantitative Text Analysis, Trinity College Dublin and University College Dublin.
2018: Introduction to Quantitative Text Analysis Using R, Methods Center of the Bremen International Graduate School of Social Sciences.
2018: Introduction to Quantitative Text Analysis Using Quanteda, WZB Berlin Social Science Center (with Kohei Watanabe).
2017: Data Wrangling and Visualisation Using R, Trinity Research in Social Science.
2018: Winner of the Dermot McAleese Award for Teaching Excellence, Trinity College Dublin
2016: Certificate in Academic Teaching & Supporting Learning, Trinity College Dublin
I work as the Documentation Manager and Training Advisor of the Quanteda Initiative (QI), a UK non-profit organisation devoted to the promotion of open-source text analysis software, and co-author of the following R packages:
quanteda: Quantitative analysis of textual data (co-author; Winner of the Society for Political Methodology Statistical Software Award [2020])
quanteda.textmodels: Scaling models and classifiers for textual data (co-author)
readtext: Import of plain and formatted text files (co-author)
newsmap: Semi-supervised model for geographical document classification (co-author)
Below you can find tutorials, cheatsheets, and vignettes I have authored as a member of the Quanteda Initiative.
quanteda cheat sheet : a cheat sheet with the most important functions
Textual data visualization: plot word frequencies, wordclouds and results of text scaling models
readtext vignette: import a variety of text files into R
quanteda’s features: comparison of quanteda to alternative R and Python packages for quantitative text analysis
Text Analysis with R for Students of Literature: replicate the analysis from Matthew Jockers’ book with quanteda
Quantitative Social Science: An Introduction: replicate the part on text analysis from Kosuke Imai’s book with quanteda
The Irish Polling Indicator is a joint project with Tom Louwerse. We combine all Irish opinion polls for the Dáil Éireann by Behaviour & Attitudes, Ipsos MRBI, Ireland Thinks, Millward Brown, Panelbase, and Red C Research into daily estimates of public support for the parties. The website of the Polling Indicator provides various visualisations and detailed information on the underlying method.
The figure below (updated automatically after the release of new polls) summarises the most recent estimates of party support:
01.2020– | Assistant Professor (Lecturer) and Ad Astra Fellow University College Dublin – School of Politics and International Relations – Connected_Politics Lab (founding member) |
01.2019–06.2020 | Senior Researcher (Oberassistent) University of Zurich, Department of Political Science |
09.2015–12.2018 | PhD in Political Science Trinity College Dublin, Department of Political Science |
09.2017–05.2018 | Postgraduate Certificate in Statistics Trinity College Dublin, School of Computer Science and Statistics |
09.2014–08.2015 | Master of Science in Politics and Public Policy Trinity College Dublin, Department of Political Science |
10.2011–06.2014 | Bachelor of Arts in Political Science and Sociology University of Bonn, Department of Political Science and Sociology |
08.2019–09.2019 | Visiting Research Fellow Kobe University, Graduate School of Law |
06.2019–07.2019 | Visiting Researcher Trinity College Dublin, Department of Political Science |
05.2018–06.2018 | Guest Researcher EUROLAB at GESIS – Leibniz Institute for the Social Sciences, Cologne |