Abstract
Microblogs play an important role in online social communications. Different from ordinary pieces of information, some hot topics and emerging news will become much more popular in a very short time with the help of this information spreading platform of microblogs. In these spreading events, messages are transmitted to a vast range of individuals through a small portion of users engaged in the information diffusion process, a.k.a. super spreaders. Gaining an awareness of super spreading phenomena and an understanding of patterns of vast-ranged information diffusion process is worthy for several tasks such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. In this paper, inspired by the analogous patterns of super spreading in both information diffusion and spread of a contagious disease, we build a parameterized model based on well-known epidemic models to characterize super spreading phenomenon of tweet message diffusion accompanied with super spreaders. Through a fitting process, parameter settings under different scenarios are obtained and the corresponding basic reproduction number is also analyzed, which indicates the degree super spreader will affect the spreading. With the help of the SAIR model, some feasible applications can be exploited.
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