  The ability to model and predict the popularity dynamics of individual user
generated items on online media has important implications in a wide range of
areas. In this paper, we propose a probabilistic model using a Self-Excited
Hawkes Process(SEHP) to characterize the process through which individual
microblogs gain their popularity. This model explicitly captures the triggering
effect of each forwarding, distinguishing itself from the reinforced Poisson
process based model where all previous forwardings are simply aggregated as a
single triggering effect. We validate the proposed model by applying it on Sina
Weibo, the most popular microblogging network in China. Experimental results
demonstrate that the SEHP model consistently outperforms the model based on
reinforced Poisson process.
