Tree-based mining for discovering patterns of reposting behavior in microblog

Abstract

Discovering behavior patterns is important in online human interaction understanding (e.g., how information is shared through reposting, what roles do people play in a conversation). As reposting has become the key mechanism for information propagation in social media (e.g. microblog) and contributes a lot to users' participation in online events, it is important to explore how repost works. Different from previous studies, we make two contributions in this work: firstly, we analyze the patterns of reposting behavior from the perspective of microblog user and employ a special mining method which successfully find interesting results; secondly, our analysis is based on the Sina Weibo, which has different characteristics with Twitter. Specifically, information flow for a certain message in Weibo is represented as a tree. Tree-based pattern mining algorithm is presented to extract a number of interesting patterns which are useful for understanding information diffusion in the Weibo network. © Springer-Verlag 2013.