Computational social science is an emerging academic research area at the intersection of computer science, statistics, and the social sciences, in which quantitative methods and computational tools are used to identify and answer social science questions. The field is driven by new sources of data from the Internet, sensor networks, government databases, crowdsourcing systems, and more, as well as by recent advances in computational modeling, machine learning, statistics, and social network analysis. \par

The related area of social computing deals with the mechanisms through which people interact with computational systems, examining how and why people contribute to crowdsourcing sites, and the Internet more generally. Examples of social computing systems include prediction markets, reputation systems, and collaborative filtering systems, all designed with the intent of capturing the wisdom of crowds. \par

Machine learning plays in important role in both of these research areas, but to make truly groundbreaking advances, collaboration is necessary: social scientists and economists are uniquely positioned to identify the most pertinent and vital questions and problems, as well as to provide insight into data generation, while computer scientists contribute significant expertise in developing novel, quantitative methods and tools. To date there have been few in-person venues for researchers in these traditionally disparate areas to interact. This workshop will address this need, with an emphasis on the role of machine learning, making NIPS an ideal venue. We hope to attract a mix of established members of the NIPS community and researchers who have never attended NIPS and will provide an entirely new perspective. \par

The primary goals of the workshop are to provide an opportunity for attendees to meet, interact, share ideas, establish new collaborations, and to inform the wider NIPS community about current research in computational social science and social computing. \par

Program Committee: Lars Backstrom (Cornell University), Jordan Boyd-Graber (University of Maryland), Jonathan Chang (Facebook), Sanmay Das (Rensselaer Polytechnic Institute), Ofer Dekel (Microsoft Research), Laura Dietz (Max Planck Institute for Computer Science), Arpita Ghosh (Yahoo! Research), John Horton (Harvard University), Shaili Jain (Yale University), David Jensen (University of Massachusetts, Amherst), Lian Jian (Annenberg School of Communications, University of Southern California), Edith Law (Carnegie Mellon University), David Lazer (Political Science and Computer Science, Northeastern University \& Kennedy School of Government, Harvard University), Winter Mason (Yahoo! Research), Andrew McCallum (University of Massachusetts, Amherst), Mary McGlohon (Google), Daniel Ramage (Stanford University), Noah Smith (Carnegie Mellon University), Victoria Stodden (Yale Law School), and Sid Suri (Yahoo! Research). \par

Please visit the workshop website for up-to-date information about the schedule, including the schedule of posters.