Big data, big research? Opportunities and constraints for computer supported social science.

Jürgen Pfeffer (Carnegie Mellon University, Pittsburgh)

KEYNOTE | Zeit: FR 09:20 – 10:30 | Ort: Presseclub Concordia

Big data is one of the catch phrase of the current decade. In science, politics, and media many hopes and fears are connected to this term. Billions of data points are created online every day and the ubiquitous proliferation of social media, like Facebook and Twitter, incorporate the activity of millions of people into this data stream. The traces of these activities in databases offer exciting ways to study new (as well as very old) social science research questions, e.g., how does information diffuse, how do groups form, what are the dynamics of political discussion, and many more. Big data research is also well funded. While other research areas have been hit hard by the US sequester and budget cuts in many countries, the Obama administration announced the “Big Data Research and Development Initiative” in 2012 to fund research in this context with $200 Million dollars.

Since then, it seems to be quite obvious that this is connected to data collection and surveillance of hundreds of millions of people around the world accomplished by governments and their intelligence agencies. Beside the implications on privacy, a diverse array of methodological issues arises when analyzing large scale social systems. Many traditional algorithms have been developed to analyze the relational activities of small groups. Applying the same methods to very large datasets can be impossible because of calculation time. But there are also substantial conceptual questions embedded in analyzing these data. For instance, are the individuals in the dataset a good representation of the population that we want to study and are the data that we collect from a computer supported system good samples of the actual data that are stored by the system? And how can we identify and maybe measure possible bias?

 
pfeffer_smallJürgen Pfeffer earned a B.S. degree in Computer Science, an M.S. degree in Computer Science Management, and a Ph.D. degree in Business Informatics from Vienna University of Technology. He was working in industry and non-university research institutes for ten years before he joined the Institute for Software Research at Carnegie Mellon University in Pittsburgh (USA) as a Post-Doctoral Associate in December 2010. Since September 2012 Jürgen Pfeffer has been an Assistant Research Professor in the School of Computer Science at Carnegie Mellon University.
Pfeffer’s research focus lies in the computational analysis of organizations and societies with a special emphasis on large-scale systems. He is particularly interested in methodological and algorithmic questions as well as challenges arising from analyzing such systems. His research combines traditional network analysis and dynamic network analysis theories and methods with up-to-date science from the areas of visual analytics, geographic information systems, system dynamics, and data mining. Most of Pfeffer’s work is at the intersection of computer science and social science.