摘要
Predicting and modeling of items popularity on web 2.0 have attracted great attention of many scholars. From the perspective of information competition, we propose a probabilistic model using the branching process to characterize the process in which micro-blogging gains its popularity. The model is analytically tractable and can reproduce several characteristics of empirical micro-blogging data on Sina micro-blog, the most popular micro- blogging system in China. We find that the information competition on micro-blog network leads to the decay of information popularity obeying power law distribution with exponent about 1.5, and the value is similar to the exponent of degree distribution of micro-blog network. Furthermore, the mean popularity is decided by the probability of innovating a new message. Our work presents evidence supporting the idea that two distinct factors affect information popularity： information competition and social network structure.
Predicting and modeling of items popularity on web 2.0 have attracted great attention of many scholars. From the perspective of information competition, we propose a probabilistic model using the branching process to characterize the process in which micro-blogging gains its popularity. The model is analytically tractable and can reproduce several characteristics of empirical micro-blogging data on Sina micro-blog, the most popular micro- blogging system in China. We find that the information competition on micro-blog network leads to the decay of information popularity obeying power law distribution with exponent about 1.5, and the value is similar to the exponent of degree distribution of micro-blog network. Furthermore, the mean popularity is decided by the probability of innovating a new message. Our work presents evidence supporting the idea that two distinct factors affect information popularity： information competition and social network structure.
基金
Supported by the National Natural Science Foundation of China under Grant Nos 71601005 and 71231002
the National Basic Research Program of China under Grant No 2013CB329604
