With the advent of Web 2.0, Social Computing has emerged as one of the hot research topics recently. Social Computing involves the investigation of collective intelligence by using computational techniques such as machine learning, data mining, natural language processing, etc. on social behavior data collected from blogs, wikis, clickthrough data, query logs, tags, etc. from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this tutorial, we will introduce Social Computing and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. Moreover, we will discuss the challenging issues involved in Social Computing in the context of theory and models of social networks, mining of spatial and temporal social information, ways to deal with partial and incomplete information in the social context, scalability and algorithmic issues with social computational techniques, and security & privacy issues. Some potential social computing applications for discussion would include collaborative filtering, query log processing, learning to rank, large graph and link algorithms, etc.
