About
I am a Professor of Data Science at the Faculty of Economics and Business Studies of the University of Giessen. Before joining the University of Giessen, I worked as a postdoctoral researcher in machine learning at the University of Oxford. Prior to that, I headed my own research group at the University of Freiburg, where I also obtained my Ph.D. in Information Systems. My research focuses on data science methods and computational techniques for understanding and predicting human decision-making in the digital age. Current research projects apply machine learning and natural language processing to a broad selection of topics, including (1) social networks, (2) recommender systems, and (3) financial markets. Apart from academic research, I am a passionate programmer and have developed multiple widely used R packages (> 150,000 downloads via CRAN) for text mining and machine learning.
Featured Research
Negativity Drives Online News Consumption
Co-authored with Claire E. Robertson (NYU), Kaoru Schwarzenegger (ETH Zurich), Phillip Parnamets (Karolinska Institutet), Jay J. Van Bavel (NYU), Stefan Feuerriegel (LMU Munich)
- Research paper at Nature Human Behaviour (open access)
- Media coverage (selection): The Atlantic, ARD, FAZ, Deutschlandfunk, Psychology Today, Heise, ORF
Community Notes Increase Trust in Fact-Checking on Social Media
- Research paper at PNAS Nexus (open access)
- Interview with FAZ
- Media coverage in The Washington Post
New Teaching Materials
Slides: Exploratory Text Analysis in R
This slide deck presents an introduction to explanatory text analysis in R. The main learning goals are:
- Exploratory text analysis: Learn how to gain an initial understanding of text data
- Tidy text analysis: Learn how to perform text analysis in a “tidy” way using tidytext
- Corpus analyis: Understand how to explore text corpora and perform tf-idf document weighting in R
The slides can be downloaded here.
Slides: Tidy Data Manipulation in R
This slide deck presents an introduction to tidy data manipulation in R. The main learning goals are:
- Tidy data manipulation: Learn how to manipulate data using the “dplyr” R-package
- Pipe operator: Learn how increase code readability using pipes
- Joins: Learn how to efficiently join separate datasets in R
The slides can be downloaded here.