Scalable Learning of Collective Behavior Based on Sparse Social Dimensions

The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in a large scale. In this paper, the authors aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can one infer the behavior of unobserved individuals in the same network? A social-dimension based approach is adopted to address the heterogeneity of connections presented in social media.