Ken’s research focuses on automated, quantitative methods of processing large amounts of textual and other forms of big data – mainly political texts and social media – and the methodology of text mining. He is the creator and co-author of several popular R packages for text analysis, including quanteda, spacyr, and readtext. He has published extensively on applications of measurement and the analysis of text as data in political science, including machine learning methods and text coding through crowd-sourcing, an approach that combines statistical scaling with the qualitative power of thousands of human coders working in tandem on small coding tasks.
His substantive research interests include comparative party competition, the European Parliament, electoral systems, and transitions to democracy. Much of his recent work involves estimating the electoral effects of campaign spending. He is also a leading specialist on Hungarian elections and the Hungarian electoral system. His methodological interests include statistical methodologies for the social sciences, especially those relating to measurement and quantitative text analysis. Recent data large-scale measurement projects in which he has been involved include estimating policy positions of political parties through expert surveys, manifesto coding, and text analysis.