Detecting bursty events in collaborative tagging systems

Collaborative tagging systems have emerged as an ubiquitous way to annotate and organize online resources. The users' tagging actions over time reflect the changing of their interests. In this paper, we propose to detect bursty tagging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. We exploit the sliding time intervals to extract bursty features from large tag corpora as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. An experimental study demonstrates the superiority of our approach.