In the context of social networks, it is interesting to formalize and locate bursts of activities amongst users, related to a particular event and to report sets of socially connected users participating in such bursts. Such collections present new opportunities for understanding social events, render new ways of online marketing, and provide useful information for IR applications such as query expansion and diversified search.

In this presentation, we propose two models to define the notion of "burst". The first model (intrinsic burst model) takes the intrinsic characteristics of each user into account to recognize the bursty behaviors; while the second model (social burst model) considers neighbors' influences when identifying bursts. We provide two linear algorithms to detect bursts based on the proposed models. These algorithms have been extensively evaluated on a month of full Twitter dataset certifying the practicality of our approach. A detailed qualitative study of our techniques is also presented.