Call for Papers

December 11th, 2010, in conjunction with NIPS 2010
Whistler, BC, Canada

1. The schedule is updated! This is different from the NIPS booklet.

1. Check here for paper acceptance.

1. Follow us at learning4social@twitter

Social computing aims to support the online social behavior through computational methods. The explosion of the Web has created and been creating social interactions and social contexts through the use of software, services and technologies, such as blogs, microblogs (Tweets), wikis, social network services, social bookmarking, social news, multimedia sharing sites, online auctions, reputation systems, and so on. Analyzing the information underneath the social interactions and social context, e.g., community detection, opinion mining, trend prediction, anomaly detection, product recommendation, expert finding, social ranking, information visualization, will benefit both of information providers and information consumers in the application areas of social sciences, economics, psychologies and computer sciences. However, the large volumes of user-generated contents and the complex structures among users and related entities require effective modeling methods and efficient solving algorithms, which therefore bring challenges to advanced techniques in machine learning. There are three major concerns:

1. How to effectively and accurately model the related task as a learning problem?
2. How to construct efficient and scalable algorithms to solve the learning task?
3. How to fully explore and exploit human computation?

This workshop aims to bring together researchers and practitioners interested in this area to share their perspectives, identify the challenges and opportunities, and discuss future research/application directions through invited talks, panel discussion, and oral/poster presentations.

Topics of interest include, but are not limited to:

• Communities discovery and analysis in social networks
• Sentiment analysis and opinion mining
• Topic detection in instant message systems
• Fusion of information from multiple blogs, rating systems, and social networks
• Classification and clustering of blogs, tweets based on the content and link structure
• Extraction and visualization of network structures and user relationships
• Trend prediction and dynamics of social networks
• Authority identification and influence measurement in social networks
• Collaborative filtering and recommendations systems
• Temporal analysis on social network topologies
• Social ranking and social tagging
• Anomaly detection in social networks
• Personalization for search and social interaction
• Scalable algorithms dealing with large size of blogosphere and networks
• Statistics for Social Science
• Human computation and social games
• Privacy protection in social networks
• Online advertising
• Graph mining algorithms
• Large scale algorithms
• Parallel or distributed learning algorithms
• Online learning algorithms

We invite papers solving the problems in social computing using machine learning methods, such as statistical methods, graphical models, graph mining methods, matrix factorization, learning to rank, optimization, temporal analysis methods, information visualization methods, transfer learning, and others.

Zenglin Xu, Purdue University
Irwin King, The Chinese University of Hong Kong
Shenghuo Zhu, NEC Labs of America
Alan Qi, Purdue University
Rong Yan, Facebook
John Yen, Penn State University

• Brian Davison, Lehigh University, US
• Hongbo Deng, University of Illinois at Urbana-Champaign, US
• Tina Eliassi-Rad, Rutgers University, US
• Bin Gao, Microsoft Research Asia, China
• Daniel Gatica-Perez, Idiap research institute, Switzerland
• Lise Getoor, University of Maryland, College Park, US
• Hao Ma, The Chinese University of Hong Kong, Hong Kong
• Alejandro Jaimes, Yahoo! Research Europe and Latin America, Spain
• Jihie Kim, USC Information Sciences Institute, US
• Nick Koudas, University of Toronto, Canada
• Kristina Lerman, USC Information Sciences Institute, US
• Ee-Peng Lim, Singapore Management University, Singapore
• Huan Liu, Arizona State University, US
• Sebastian Michel, Saarland University and MPI Informatics, Germany
• Mohammad Mahdian, Yahoo! Labs, US
• Jennifer Neville, Purdue University, US
• Lizhen Qu, Max-Planck Institute for Informatics, Germany
• Steffen Rendle, University of Hildesheim, Germany
• Vikas Sindhwani, IBM Watson Research Center, US
• Jie Tang, Tsinghua University, China
• Lei Tang, Yahoo! Labs, US
• Volker Tresp, Siemens Corp., Germany
• Laurence T. Yang, St Francis Xavier University, Canada
• Qiang Yang, Hong Kong University of Science and Technology, Hong Kong
• Kai Yu, NEC Labs America, US
• Tom C. Zhou, The Chinese University of Hong Kong, Hong Kong
• Feida Zhu, Singapore Management University, Singapore

• Lee Giles, Penn State University
• Lars Backstrom, Facebook
• Eric Xing, Carnegie Mellon University

• Submission: 23:59 PDT, October 22nd, 2010
• Acceptation Notification: Extended to November 7th, 2010
• Camera Ready: November 25th, 2010
• Workshop: December 11th, 2010

Submission Site: Microsoft CMT for Workshop of Machine Learning for Social Computing 2010.

All submissions must be in pdf format. Papers are limited to maximum six pages, including figures and tables, in the NIPS style (which can be obtained from http://nips.cc/PaperInformation/StyleFiles). One more page for references is allowed.

The review process is double blind, so do not include any author information.

• The proceedings of the workshop will appear in the JMLR Workshop and Conference Proceedings series.
• The camera ready version for JMLR c&w proceedings can be found from sample-papers.zip, which is slightly modified from the JMLR Style File.