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A user profile contains information about a user.
A substantial effort has been made so as to understand users’ behavior through
analyzing their profile data. Online social networks provide an enormous amount
of such information for researchers. Sina Weibo, a Twitter-like microblogging
platform, has achieved a great success in China although studies on it are
still in an initial state. This paper aims to explore the relationships among
different profile attributes in Sina Weibo. We use the techniques of
association rule mining to identify the dependency among the attributes and we
found that if a user’s posts are welcomed, he or she is more likely to have a
large number of followers. Our results demonstrate how the relationships among
the profile attributes are affected by a user’s verified type. We also put some
efforts on data transformation
and analyze the influence of the statistical properties of the data
distribution on data discretization.