  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 statically and
dynamically analyze 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 to power-law distribution. Conversely, the
maximum number of famous followees in one category for each user shows
power-law property. To our best knowledge, there is no research work on this
topic with human-chosen famous entity dataset in real life. These findings
might be helpful in microblogging marketing and user classification.
