Research and applications in knowledge discovery and data mining increasingly address some of the most fundamental questions of social science: What determines the structure and behavior of social networks? What influences consumer and voter preferences? How does participation in social systems affect behaviors such as fraud, technology adoption, or resource allocation? Often for the first time, these questions are being examined by analyzing massive data sets that record the behavior and interactions of individuals in physical and virtual worlds.

A new kind of scientific endeavor - computational social science - is emerging at the intersection of social science and computer science. The field draws from a rich base of existing theory from psychology, sociology, economics, and other social sciences, as well as from the formal languages and algorithms of computer science. The result is an unprecedented opportunity to revolutionize the social sciences, expand the reach and impact of computer science, and enable decision-makers to understand the complex systems and social interactions that we must manage in order to address fundamental challenges of economic welfare, energy production, sustainability, health care, education, and crime.

Computational social science suggests an impressive array of new tasks and technical challenges to researchers and practitioners of KDD. These include modeling complex systems with temporal, spatial, and relational dependence; identifying cause and effect rather than mere association; modeling systems with feedback; and conducting analyses in ways that protect the privacy of individuals. Many of these challenges interact in fundamental ways that are both surprising and encouraging. Together, they point to an exciting new future for knowledge discovery and data mining.