About Me

Data Science

Since some years I define myself as a data scientist with a particular focus on text-as-data procedures. Of course, I am still interested in political science as most of my research and applications focus on exactly this domain: Explain and predict social science phenomena by technical means. The term “technical” combines various approaches like

  • machine learning
  • regressions
  • visualizations

More recently, I also closely collaborate with the law faculty – transferring my experiences to a domain that makes great progress in adopting text-as-data applications. Here, I want to draw your attention to the research project Künstliche Intelligenz und richterliche Entscheidungsfindung.

Overall, my research and teaching focus in data science is organized around three larger themes: empirical and computational methodology, deliberative communication, and transfer services. I do not see these areas as separate, but as mutually dependent and complementary research strategies. Still, I provide some separate insights into these three pillars:

Empirical and Computational Methodology

The empirical and computational focus of my research is the development and application of text-as-data procedures. For instance, as part of the VisArgue project, I have (co-)developed an automated textual measure for deliberative quality. Also, I was involved in the ADD-up project conceptualizing and testing visual applications to explore and analyze deliberative discourse in real time. Finally, I coordinate the Deliberation Laboratory (DeLab) – an interdisciplinary project aiming to improve social media conversations by using AI.

Deliberative Communication

Within deliberative communication research, I concentrate on explaining democratic decision-making processes and extracting the role and dynamics of communication within such processes. I aim for opening the black box of conversation dynamics. For instance, I am interested in explaining the link between personality and argumentative behavior: Do people scoring high on conscientiousness provide more justifications? Which types of justification are addressed more often? Also, I strive for identifying the momentums within conversations in which participants get persuaded by others.

Transfer Services

The third research pillar, transfer services, aims at designing intervention systems: With ADD-up, we propose a real-time visual intervention system that supports participants in achieving a high deliberative quality. The project DeLab aims at developing a virtual moderator that uses artificial intelligence to recognize when discussions in social media are becoming increasingly destructive in character. The virtual moderator should then intervene in the discussion and help to prevent escalation. Both projects are funded by the Volkswagen Foundation.