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 localitybased 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 Twit

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Krishna Yeswanth Kamath, James Caverlee
• Keywords: CIKM 2010, Information Technology, Localitybased Clustering Approach, Massive Social Messaging, Social Messaging Systems