Application of Association Rule Mining Theory in Sina Weibo

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.