About
I am an Associate Professor of Political Science at the Department of Political Science at Aarhus University, Denmark. I study political communication, emotions and morality, and political behavior, with a methodological focus on computational text analysis and natural language processing.
Previously, I was a PhD researcher at the European University Institute in Florence and held visiting positions at the Hertie School Data Science Lab and the University of Wisconsin-Madison. My research has been supported by the Fulbright Schuman Scholarship, and my work has received awards including the Rudolf Wildenmann Prize. I publish in leading journals, including The Journal of Politics, Political Analysis, Comparative Political Studies, and the British Journal of Political Science.
Research interests
Data I work with
- 0parliamentary speeches (174 million sentences) from ten Western democracies since the 1960s, plus all 16 German state parliamentsPerspectives on Politics, forthcoming · iScience 2024 · Political Behavior 2021
- 0party manifestos (675,000 sentences) from ten Western democracies — Austria, Canada, Denmark, Germany, the Netherlands, Spain, Sweden, Switzerland, the UK and the US — 1960 to todayPerspectives on Politics, forthcoming · BJPS
- 0tweets and replies from citizens, alongside the Twitter/X accounts of MPs, parties and news mediaPolitical Psychology 2022 · PSRM 2024 · JOP 2025 · Work in progress
- 0Telegram posts from 300 far-right channels, linked to a new daily dataset of 11,000+ politically motivated offences against politicians, 2019–2025Work in progress
- 0party press releases and tweets from three European countries, and Economist articles spanning 177 years, 1843–2020Political Psychology 2021 · Comparative Political Studies 2024
- 0crowd-coded sentences in six languages, the training data behind my classifiersPolitical Analysis · Tools
- 0kinds of context linked to text: wind-turbine construction records, daily weather, COVID-19 case counts, election timing, party positions, and 3 million sentences of film and TV dialogue as a benchmarkJOP 2025 · iScience 2024 · Perspectives on Politics, forthcoming
- 0open-source tools for measuring emotion and morality in text, free for academic researchTools
How I work with it
- Fine-tuned transformers
- Custom dictionaries
- Word embeddings
- Crowd-coding & validation
- Staggered difference-in-differences
- Vector autoregression
- Panel fixed effects
- Cross-national comparison
Measuring what politicians say · explaining why and with what effect
A slice of the space my methods work in: 64 German terms from the ed8 emotion dictionary, placed by the word2vec vectors trained for Widmann & Wich (2022) and projected to two dimensions (t-SNE). Positive and negative vocabularies separate on their own — hover a word or an emotion.
- Anger
- Fear
- Disgust
- Sadness
- Joy
- Enthusiasm
- Pride
- Hope