Understanding user behavior through URL analysis in Sina tweets

Abstract

As the popularity of online social networks, the user behavior in cyber-world might probably become a mirror of the user in physical world. Therefore, understanding online user’s behavior is an interesting yet challenging task. This paper aims to analyze URLs in Sina tweets, the largest Chinese Twitter, to facilitate the understanding of the user behavior. In particular, we first provide some statistics data to show the global behavior. Then, we take a close look to users who publish similar URLs frequently to track their abnormal behaviors. By observing the contents and publishing time of these users, we classify users with commercial purposes into several types, and thus showcase some interesting cases to validate our classification.