Bursty event detection from collaborative tags

Collaborative tagging have emerged as a ubiquitous way to annotate and organize online resources. As a kind of descriptive keyword, large amount of tags are created and associated to multiple types of resources, e. g., web pages, photos, videos and tweets. Users' tagging actions over time reflect their changing interests. Monitoring and analyzing the temporal patterns of tags can provide important insights to trace hot topics on the web. Existing work focuses on deriving temporal patterns for individual tags. However, there exist remarkable correlations among tags assigned to online resources. In this paper, we propose a new approach 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. This kind of bursty tagging event generally corresponds to a real life event. It profiles the events with more representative and comprehensible clues. The proposed approach is divided into three stages. We exploit the sliding time intervals to extract bursty features as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. We discuss the choice of similarity and granularity for event detection. After that, we further utilize an automatically generated tag taxonomy to organize bursty events to facilitate the burst oriented navigation and analysis. The experimental study on a large real data set demonstrates the superiority of our new approach.