Identifying hotspots on the real-time web

We study the problem of automatically identifying ``hotspots'' on the real-time web. Concretely, we propose to identify highly-dynamic ad-hoc collections of users -- what we refer to as crowds -- in massive social messaging systems like Twitter and Facebook. The proposed approach relies on a message-based communication clustering approach over time-evolving graphs that captures the natural conversational nature of social messaging systems. One of the salient features of the proposed approach is an efficient locality-based clustering approach for identifying crowds of users in near real-time compared to more heavyweight static clustering algorithms. Based on a three month snapshot of Twitter consisting of 711,612 users and 61.3 million messages, we show how the proposed approach can efficiently and effectively identify Twitter-based crowds relative to static graph clustering techniques at a fraction of the computational cost.