About

I am a Professor of Data Science at the Department of Business and Economics 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 for understanding and predicting human decision-making in the digital age. Current research projects typically combine quantitative approaches to causal inference with methods from AI (e.g., machine learning, natural language processing, computer vision) across a range of domains, including social networks, online media, and digital markets. Apart from academic research, I am a passionate programmer and have developed multiple widely used R packages (­> 200,000 downloads via CRAN) for text mining and machine learning.

 

Featured Research

Negativity Drives Online News Consumption

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here, we analyze the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ∼105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more prevalent than negative words, we found that negative words in news headlines increased consumption rates (and positive words decreased consumption rates). For a headline of average length, each additional negative word increased the click-through rate by 2.3% Our results contribute to a better understanding of why users engage with online media.

Co-authored with Claire E. Robertson (NYU), Kaoru Schwarzenegger (ETH Zurich), Phillip Parnamets (Karolinska Institutet), Jay J. Van Bavel (NYU), Stefan Feuerriegel (LMU Munich)

Community-Based Fact-Checking Reduces the Spread of Misleading Posts on Social Media

Social media platforms increasingly rely on community-based fact-checking systems such as X’s Community Notes to combat misinformation at scale. In this study, we analyze more than 431 million reposts across 237,180 fact-checked cascades and provide large-scale causal evidence that community notes reduce the subsequent spread of misleading posts by 61.2% on average. We further find that community notes increase the likelihood that users delete misleading posts by 94.3%. However, notes often appear too late to prevent the early, most viral stage of diffusion, limiting their overall system-wide impact. Our findings highlight both the promise and current limitations of community-based fact-checking systems in reducing misinformation on social media.

 

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.