Cognitive-Costed Agent Model of the Microblogging Network

Abstract

Microblogging is a new paradigm spreading in social web services that provides us a light-weight, speedy way of communication. In the microblogging system, users post short messages just as quickly as chatting. Users can easily communicate with each other. However, the system brings users huge cognitive costs since they always need to follow up their friends' posts every second. Can such cognitive costs affect the structure of the microblogging network? Here we extracted data from the most major microblogging service: Twitter. We find that the network structure in Twitter has some features: power law decay in the small degree range, link reciprocity and asymmetry between distributions of the in-degree and out-degree. To explain such characteristics, we introduce a simple stochastic agent model based on the Barabasi-Albert model. With the mathematical analysis and computational experiments, we confirm that even such a simple model explains well the behavior of the observed data.