General Information

The call for papers (plain text) is available here.


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. 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. 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 are able to contribute significant expertise in developing novel, quantitative methods and tools. The primary goals of this workshop are to provide an opportunity for attendees from diverse fields 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. The inaugural workshop on Computational Social Science and the Wisdom of Crowds (held in 2010) brought together experts from fields as diverse as political science, psychology, economics, and machine learning, connecting researchers with common goals but disparate methods and audiences, and we aim to attract a similar breadth of contributions this year.

Topics of Interest

We welcome contributions on both theoretical models and empirical work, as well as everything in between, including but not limited to the following areas of research:

  • Automatic aggregation of opinions or knowledge
  • Incentives in social computation (e.g., game-theoretic approaches)
  • Prediction markets/information markets
  • Studies of events and trends (e.g., in politics)
  • Quality control for user generated content
  • Analysis of and experiments on distributed collaboration and consensus-building, including crowdsourcing (e.g., Mechanical Turk) and peer-production systems (e.g., Yahoo! Answers)
  • Group dynamics and decision-making
  • Modeling network interaction content (e.g., analysis of blog posts, tweets, emails, chats, etc.)
  • Social networks
  • Games with a purpose

Paper Submission

Papers are limited to four content pages, including figures and tables, and must follow the NIPS 2011 format; however, an additional fifth page containing only cited references is permitted. Papers should not be anonymized (i.e., you should uncomment or add \nipsfinalcopy in your .tex file prior to submitting). Accepted papers will be made available on the workshop website; however, the workshop's proceedings can be considered non-archival, meaning contributors are free to publish their work in archival journals or conferences. Accepted papers will be either presented as a talk or poster. Paper submissions should be emailed to nipscssworkshop at gmail dot com with a subject line of "NIPS CSS 2011: XXX" where "XXX" is the title of the paper submission.

Important Dates

  • Submission deadline: October 7, 2011
  • Notification of acceptance: November 11, 2011
  • Final versions of accepted papers due: November 30, 2011
  • Workshop date: December 17, 2011


  • 7:30–7:40 Opening Remarks
  • 7:40–8:25 Invited Talk: "Machine Learning and Computational Social Science: Intersections and Collisions" by David Jensen, University of Massachusetts Amherst (Computer Science) [slides]
  • 8:25–8:45 Contributed Talk: "A Text-based HMM Model of Foreign Affair Sentiment" by Sean Gerrish and David Blei [paper | slides]
  • 8:45–9:25 First Poster Session and Coffee Break
  • 9:25–10:10 Invited Talk: "The Rise of Molecules and Machines: Three Decades of Intellectual Change in Academia" by Daniel McFarland, Stanford University (Education)
  • 10:10–10:30 Contributed Talk: "A Wisdom of the Crowd Approach to Forecasting" by Brandon M. Turner and Mark Steyvers [paper | slides]
  • 10:30–4:00 Ski Break
  • 4:00–4:45 Invited Talk: "Understanding the players from how they play (Halo)" by Winter Mason, Stevens Institute of Technology (Technology Management) [slides]
  • 4:45–5:05 Contributed Talk: "Learning Performance of Prediction Markets with Kelly Bettors" by Alina Beygelzimer, John Langford, and David M. Pennock [paper | slides]
  • 5:05–5:25 Contributed Talk: "Approximating the Wisdom of the Crowd" by Seyda Ertekin, Haym Hirsh, and Cynthia Rudin [paper | slides]
  • 5:25–6:05 Second Poster Session and Coffee Break
  • 6:05–6:50 Invited Talk: "Forecasting: Expectations, Intentions, and Confidence" by David Rothschild, Yahoo! Research [slides]
  • 6:50–7:35 Invited Talk: "Quality Assurance and Connections with Machine Learning" by Panagiotis Ipeirotis, New York University (Information, Operations, and Management Sciences) [slides]
  • 7:35–7:45 Closing Remarks and Wrap-up

First poster session:

Second poster session:

Invited Speakers

Related Workshops

The website for last year's workshop is here.

Program Committee

  • Alexander Strehl, Facebook
  • Brendan O'Connor, Carnegie Mellon University
  • Bruce Desmarais, University of Massachusetts Amherst
  • Chris Callison-Burch, Johns Hopkins University
  • David Jensen, University of Massachusetts Amherst
  • David Mimno, Princeton University
  • Deepak Ganesan, University of Massachusetts Amherst
  • Edith Law, Carnegie Mellon University
  • Edoardo Airoldi, Harvard University
  • Foster Provost, New York University
  • Gabriele Paolacci, Ca' Foscari University of Venice
  • Haoqi Zhang, Harvard University
  • John Horton, oDesk Corporation
  • Jure Leskovec, Stanford University
  • Justin Grimmer, Stanford University
  • Kristen Grauman, University of Texas at Austin
  • Lester Mackey, University of California, Berkeley
  • Mark Dredze, Johns Hopkins University
  • Michael Buhrmester, University of Texas at Austin
  • Rahul Sami, University of Michigan
  • Sandy Pentland, Massachusetts Institute of Technology
  • Sean Gerrish, Princeton University
  • Thore Graepel, Microsoft Research
  • Yiling Chen, Harvard University


Thanks to our sponsor:

  • Microsoft Research