Individual popularity and activity in online social systems

We propose a stochastic model of web user behaviors in online social systems, and study the influence of the attraction kernel on the statistical property of user or item occurrence. Combining the different growth patterns of new entities and attraction patterns of old ones, different heavy-tailed distributions for popularity and activity which have been observed in real life, can be obtained. From a broader perspective, we explore the underlying principle governing the statistical feature of individual popularity and activity in online social systems and point out the potential simple mechanism underlying the complex dynamics of the systems.