Online Analysis Of Information Diffusion In Twitter

The advent of social media has facilitated the study of information diffusion, user interaction and user influence over social networks. The research on analyzing information spreading focuses mostly on modeling, while analyses of real-life data have been limited to small, carefully cleaned datasets that are analyzed in an offline fashion. In this paper, we present an approach for online analysis of information diffusion in Twitter. We reconstruct so-called inforrnation cascades that model how information is being propagated from user to user from the stream of messages and the social graph. The results show that such an inference is feasible even on noisy, large-scale, rapidly produced data. We provide insights into the impact of incomplete data and the effect of different influence models on the cascades. The observed cascades show a significant amount of variety in scale and structure.