Identifying hotspots on the real-time web Conference Paper uri icon


  • 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. © 2010 ACM.

author list (cited authors)

  • Kamath, K. Y., & Caverlee, J.

citation count

  • 2

editor list (cited editors)

  • Huang, J., Koudas, N., Jones, G., Wu, X., Collins-Thompson, K., & An, A.

publication date

  • January 2010