  Online social networks (OSNs) are changing the way in which the information
spreads throughout the Internet. A deep understanding of the information
spreading in OSNs leads to both social and commercial benefits. In this paper,
we characterize the dynamic of information spreading (e.g., how fast and widely
the information spreads against time) in OSNs by developing a general and
accurate model based on the Interactive Markov Chains (IMCs) and mean-field
theory. This model explicitly reveals the impacts of the network topology on
information spreading in OSNs. Further, we extend our model to feature the
time-varying user behaviors and the ever-changing information popularity. The
complicated dynamic patterns of information spreading are captured by our model
using six key parameters. Extensive tests based on Renren's dataset validate
the accuracy of our model, which demonstrate that it can characterize the
dynamic patterns of video sharing in Renren precisely and predict future
spreading tendency successfully.
