With the advent of Web 2.0, Social Computing has emerged as one of the hot research topics recently. Social Computing involves the collecting, extracting, accessing, processing, computing, visualizing, etc. of social signals and information. More specifically, this tutorial places special emphases in machine learning, data mining, information retrieval, and other computational techniques involved in collective intelligence processing of social behavior data collected from blogs, wikis, clickthrough data, query logs, tags, etc., and from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this tutorial, I plan to give an introduction to Social Computing and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. The topics include social network theory and modeling, graph mining, query log processing, learning to rank, recommender systems, human computation, etc. The tutorial is prepared for machine learning, web mining, and information retrieval researchers who are interested in computational approaches to social computing.

Irwin King's research interests include machine learning, web intelligence & social computing, data mining, and multimedia processing, including image, video, and text. More specifically, he is interested in applying learning techniques in social computing, e.g., large graph algorithms, ranking problems, Q&A systems, etc. One notable system he has developed is the VeriGuide System, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese.

• [2008, article]

  Luis von Ahn and Laura Dabbish, "Designing games with a purpose," Commun. ACM, vol. 51, iss. 8, pp. 58-67, 2008.

• [2009, inproceedings]

  Man-Ching Yuen, Ling-Jyh Chen, and Irwin King, "A Survey of Human Computation Systems," in Proc. CSE (4), 2009, pp. 723-728.

• Wikipedia's Human-based Computation