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 research area of social computing deals with the mechanisms through which people interact with computational systems, examining how and why people contribute user-generated content. 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 areas. For this special issue, we invite high quality submissions on work at the intersection of machine learning and any aspect(s) of social computing or computational social science, 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 and reputation mechanisms 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
PAPER SUBMISSION AND TENTATIVE SCHEDULE:
Authors are encouraged to submit high-quality, original work that has not appeared in other journals, nor is under consideration by other journals. Submissions and reviewing will be handled electronically using the standard procedures for Machine Learning Journal.
Deadline for submissions: August 1, 2012
Notification to authors: January 15, 2013
Revisions due: April 15, 2013
Winter Mason, Stevens Institute of Technology
Jennifer Wortman Vaughan, UCLA
Hanna Wallach, University of Massachusetts Amherst