In this paper, based on abundant data from Sina Weibo, we perform a comprehensive and in-depth empirical analysis of repostings and draw some conclusions. First, in regards to quantity, reposting takes up a large proportion of daily microblog activity. Second, the depth of repostings follows an exponential distribution and the first three orders of repostings hold 99 percent of the total amount of reposting, which provides an important foundation for solving the question of Influence Maximization. Third, the time interval for repostings also obeys exponential distribution. Therefore, we have built a dynamic information propagation model in terms of conclusions drawn from Weibo data and the Continuous-Time Markov Process. Due to the basis of the temporal network, our proposed model can change with the time and structure of a network, thus giving it good adaptability and predictability as compared to the traditional information diffusion model. From the final simulation results, our proposed model achieves a good predictive effect.

continuous-time Markov process, reposting, information propagation model