2nd Workshop on Computational Social Science and the Wisdom of Crowds

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 contribute significant expertise in developing novel, quantitative methods and tools.

The inaugural workshop 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. The quality of work presented was excellent and we expect the same caliber of submissions again this year. As with last year's workshop, 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.

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. To this end, the workshop will consist of invited talks, contributed talks, a poster session, a panel session, and a dinner.

We intend for the workshop to be broad enough to cover a wide variety of problems and computational techniques. Consequently, we plan to include research on theoretical models, empirical work, and everything in between, including but not limited to:

* 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., Wikipedia and Yahoo! Answers)

* Group dynamics and decision-making

* Modeling network interaction content (e.g., text analysis of blog posts, tweets, emails, chats, etc.)

* Social networks

* Games with a purpose

The workshop will address the following specific goals:

* Identify and formalize open research areas.

* Propose, explore, and discuss new questions and problems.

* Discuss how best to facilitate the transfer of research ideas between the computer and social sciences.

* Direct future work and create new application areas, novel modeling approaches, and unexplored collaborative research directions.

The workshop will be announced via email and relevant mailing lists (including the ML-NEWS, UAI, COLT, PASCAL, and topic modeling lists). We will also ask the members of our interdisciplinary program committee (currently being formed) to spread the word in their own research communities. We will construct a workshop website, containing information for prospective participants and pointers to relevant research within the computer and social sciences. Accepted submissions will be made publicly available on the website.