Abstract
In social networks, how human activity patterns affect the popularity of topics has always been the focus of research. In this paper, a quantitative temporal analysis of the dynamics of topics popularity in Sina Weibo system was provided. Firstly, the popularity time series of 1167 topics were clustered into four clusters by K-Spectral Centroid (KSC) clustering algorithm. Secondly, for each cluster, we calculated the exponents of topic popularity decay distribution alpha and the exponents of inter-activity time distribution beta, respectively. Two interesting results were found: one is that the peak fraction F of topics popularity positively correlated with the topics popularity decay exponent alpha; the other is that bursty activity patterns in social network significantly affect topics popularity dynamics: there is a positive correlation between exponent alpha and exponent beta. Finally, we proposed an extended SI (susceptible-infected) epidemic model with incorporate bursty human activity and verified the results by simulation. (C) 2021 Elsevier B.V. All rights reserved.
NSTL
Elsevier