An empirical study of how users adopt famous entities

Users of social networking services construct their personal social networks by creating asymmetric and symmetric social links. Users usually follow friends and selected famous entities that include celebrities and news agencies. In this paper, we investigate how users follow famous entities. We analyze static and dynamic data within a huge social networking service with a manually classified set of famous entities. The results show that the in-degree of famous entities does not fit the power-law distribution. Conversely, the maximum number of famous followers in one category for each user shows power-law property. To our knowledge, there is no research work on this topic on human-chosen famous entity dataset in real life and so these findings might be helpful in microblogging marketing and user classification.