Web 2.0 technologies have brought new ways of connecting people in social networks for collaboration in various on-line communities. Social Computing is a novel and emerging computing paradigm that involves a multi-disciplinary approach in analyzing and modeling social behaviors on different media and platforms to produce intelligent and interactive applications and results. In this paper, we give a brief survey of the various machine learning and computational techniques used in Social Computing by first examining the social platforms, e.g., social network sites, social media, social games, social bookmarking, and social knowledge sites, where computational methodology is required to collect, extract, process, mine, and visualize the data. We then present surveys on more specific instances of computation tasks and techniques, e.g., social network analysis, link modeling and mining, ranking, sentiment analysis, etc., that are being used on these social platforms to obtain desirable results. Lastly, we present a small subset of an extensive reference list, which contains over 140 highly relevant references relating to the recent development in the computational aspects of Social Computing.
['Irwin King', 'Jiexing Li', 'Kam Tong Chan']
A brief survey of computational approaches in Social Computing