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. 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 machine learning. 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.

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
  • Prediction markets / information markets
  • Incentives in social computation (e.g., games with a purpose)
  • Studies of events and trends (e.g., in politics)
  • Analysis of and experiments on distributed collaboration and consensus-building, including crowdsourcing (e.g., Mechanical Turk) and peer-production systems (e.g., Wikipedia)
  • Group dynamics and decision-making
  • Modeling network interaction content (e.g., text analysis of blog posts, tweets, emails, etc.)
  • Social networks

Paper Submission

Papers may be up to four pages long including references and must be in the NIPS 2010 format. 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 both wallach at cs umass edu and jenn at cs ucla edu with a subject line of "NIPS CSS 2010: XXX" where "XXX" is the title of the submission.

Important Dates

  • Submission deadline: October 8, 2010
  • Notification of acceptance: November 1, 2010
  • Final versions of accepted papers due: November 29, 2010
  • Workshop date: December 10, 2010

Schedule (Tentative)

  • 7:30–7:35 Opening remarks
  • 7:35–8:15 Invited talk: "Inferring Political Slant from Digging Patterns" by Paul Resnick, University of Michigan (School of Information) [slides]
  • 8:15–8:55 Invited talk: "Synchronous Experiments on Mechanical Turk" by Winter Mason, Yahoo! Research [slides]
  • 8:55–9:35 First Poster Session
  • 9:35–9:50 Contributed talk: "The Ideal Point Topic Model: Predicting Legislative Roll Calls from Text" by Sean Gerrish and David Blei [paper]
  • 9:50–10:30 Invited talk: "Computer-Assisted Clustering Methods for Conceptualization" by Justin Grimmer, Stanford (Political Science)
  • 10:30–3:30 Ski break
  • 3:30–3:45 Contributed talk: "Preferences in College Applications – a Nonparametric Bayesian Analysis of Top-10 Rankings" by Alnur Ali, Thomas Brendan Murphy, Marina Meila, and Harr Chen [paper | slides]
  • 3:45–4:25 Invited talk: "Markets as a Forecasting Tool " by Yiling Chen, Harvard University (Computer Science) [slides]
  • 4:25–4:40 Contributed talk: "An Optimization-Based Framework for Combinatorial Prediction Market Design" by Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan [paper | slides]
  • 4:40–4:55 Contributed talk: "Combining Human and Machine Intelligence for Making Predictions" by Yiftach Nagar and Thomas W. Malone [paper | slides]
  • 4:55–5:35 Second Poster Session
  • 5:35–6:15 Invited talk: "An Economic View of Crowdsourcing and Online Labor Markets" by John Horton, Harvard University (Public Policy) [slides]
  • 6:15–6:30 Contributed talk: "Time Critical Social Mobilization: The DARPA Network Challenge Winning Strategy" by Galen Pickard, Iyad Rahwan, Wei Pan, Manuel Cebrian, Riley Crane, Anmol Madan, and Alex (Sandy) Pentland [paper]

First poster session:

Second poster session:

Invited Speakers

Related Workshops

There are two other NIPS workshops that may be of interest to participants:

These workshops will both be taking place on December 11.

Program Committee

  • Lars Backstrom, Cornell
  • Jordan Boyd-Graber, University of Maryland
  • Jonathan Chang, Facebook
  • Sanmay Das, RPI
  • Ofer Dekel, Microsoft Research
  • Laura Dietz, Max Planck Institute
  • Arpita Ghosh, Yahoo! Research
  • John Horton, Harvard
  • Shaili Jain, Yale
  • David Jensen, UMass Amherst
  • Lian Jian, USC
  • Edith Law, CMU
  • David Lazer, Northeastern & Harvard
  • Winter Mason, Yahoo! Research
  • Andrew McCallum, UMass Amherst
  • Mary McGlohon, Google
  • Daniel Ramage, Stanford
  • Noah Smith, CMU
  • Victoria Stodden, Yale
  • Sid Suri, Yahoo! Research