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Department of Informatics Data Analytics Group

Computational Social Science

The increasing volume of available data on social systems opens new opportunities for large-scale, quantitative studies of social phenomena. Such studies can help us to better understand how humans communicate and collaborate, what makes teams productive, what mechanism are at work in successful social organizations, and how technology shapes human behavior. This research not only offers new ways to address long-standing issues in the social sciences, it is also crucial to model, design and manage socio-technical systems.

Relation between size of a team (x-axis) and productivity of its members (y-axis).

Addressing these questions, we use Big Data Science to study social organizations. In a large-scale analysis of data on more than 30,000 developers in 58 Open Source Software projects, we could validate and quantify the Ringelann effect known from social psychology and organizational theory. We could also show how coordination structures in software development teams influence the productivity of team members. Studying large bibliographic data sets, we could further prove that social aspects influence editorial processes and citation practices. Our works provide actionable insights for project management and policy-making.

Exemplary publications