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.

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.

• I Scholtes, P Mavrodiev, F Schweitzer: From Aristotle to Ringelmann: a large-scale analysis of productivity and coordination in Open Source Software projects, In Empirical Software Engineering, March 2016
• E Sarigöl, D Garcia, I Scholtes, F Schweitzer: Quantifying the effect of editor-author relations on manuscript handling times, In Scientometrics, March 2017
• E Sarigöl, R Pfitzner, I Scholtes, A Garas, F Schweitzer: Predicting Scientific Success Based on Coauthorship Networks, In EPJ Data Science, September 2014