I’m Hauke, an Assistant Professor of Computational Political Science at the Department of Political Science and the Digital Science Center of the University of Innsbruck, Austria.

I develop and apply computational methods for the comparative study of political rhetoric. For example, in my dissertation, I have examined how political text collections’ increasing volume, variety, and granularity can be harnessed to study party competition across countries and languages. For example, in a paper with Ronja Sczepanski (Sciences Po Paris), we present a novel, deep learning-based method for extracting mentions of social groups from political texts that has received the PolMeth Europe Best Poster Award for Innovative Data Science in 2022.

I’m particularly interested in the role of rhetorical strategies in democratic representation, electoral competition, and legislative politics. For example, in a Journal of Politics paper, Tarik Abou-Chadi, Pablo Barberá, Whitney Hua, and I apply machine learning methods to Twitter data to investigate how challenger and mainstream parties adapt their anti-elite rhetoric to coalition formation incentives and their standing in the polls.

Another part of my research focuses on multilingual text analysis and seeks to equip scholars and data analysts with novel tools for comparing political processes and decisions across contexts and languages. For example, in my paper “Cross-Lingual Classification of Political Texts Using Multilingual Sentence Embeddings” published in Political Analysis, I show how translation-free, state-of-the-art transfer learning strategies can enable reliable cross-lingual political text analysis. And in a Computational Communication Research paper co-authored with Fabienne Lind (University of Vienna), we provide a guide to multilingual text analysis for political and communication scientists.

Read more about my research and professional activities in my CV. And feel free to email me or contact me on Twitter!