Our research focuses on the development and application of algorithmic methods and theories for the social sciences and humanities. The social sciences and humanities are confronted with an increasing volume of data about social systems, -processes and -phenomena. At the same time, computer science and computer systems increasingly face societal, sociological, ethical and many other challenges. Both developments together require computational methods and theories for studying, modelling and shaping integrated social-computational systems.
The work of our group is thus geared towards the integration of computer science on one hand, and the social sciences and humanities on the other. In research and teaching, we develop and transfer (i) Methods for mining and modelling textual data and (ii) Methods for mining and modelling relational and sequential data.
This enables the application of computational methods to answer social science and humanities research questions, while at the same time helps to adopt social science and humanities methods and theories for tackling research challenges in computer science.
Human behavior is ofter captured in data sequences, e.g., as sequences of visited places or as sequences of visited websites. We aim to develop novel data analysis algorithms that allow for understanding such sequences
Big Data is often heterogeneous. In that regard, we aim at developing new methods that can identify interesting (exceptional) subparts of the data through approaches of Pattern Mining (in particular Subgroup Discovery and Exceptional Model Mining)
We apply data mining and knowledge discovery methods to study human behavior in online environments.
We analyze data to investigate biases and cultural differences