Modeling and reproducing retweeting dynamics in micro-blogging social networks

Micro-blogging is becoming an important platform for propagation and diffusion of news, ideas and innovations. Its unique pattern of information diffusion and dynamics has attracted many researchers. Compared to other studies, this paper provides a new perspective of analysis for the diffusion process. A multi-level structure is defined to demonstrate the retweeting dynamics. The diffusion of a particular topic is represented as the evolution of a related retweeting network, where retweeting groups and information cascades are growing and interacting. Based on this model, interesting features of the merging effect between two retweeting groups, the existence of super group and the typical topologies of information cascades are discovered and analyzed. We also find that the trend of the diffusion in the future is influenced by the diffusion of the past. This is against the implicit assumptions in some traditional diffusion models. Based on the above analysis, a propagation model with dynamic parameters is proposed to generate the retweeting network based on the data of Sina Microblog. The simulation results are consistent with the real data, and reproduce the evolution of the retweeting network.