Paper Info
Title
Transient crowd discovery on the real-time social web
Abstract
In this paper, we study the problem of automatically discovering and tracking transient crowds in highly-dynamic social messaging systems like Twitter and Facebook. Unlike the more static and long-lived group-based membership offered on many social networks (e.g., fan of the LA Lakers), a transient crowd is a short-lived ad-hoc collection of users, representing a "hotspot" on the real-time web. Successful detection of these hotspots can positively impact related research directions in online event detection, content personalization, social information discovery, etc. Concretely, we propose to model crowd formation and dispersion through a message-based communication clustering approach over time-evolving graphs that captures the natural conversational nature of social messaging systems. Two of the salient features of the proposed approach are (i) an efficient locality- based clustering approach for identifying crowds of users in near real-time compared to more heavyweight static clustering algorithms; and (ii) a novel crowd tracking and evolution approach for linking crowds across time periods. We find that the locality-based clustering approach results in empirically high-quality clusters relative to static graph clus- tering techniques at a fraction of the computational cost. 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 successfully identify and track interesting crowds based on the Twitter communication structure and uncover crowd-based topics of interest.
Year
DOI
Venue
2011
10.1145/1935826.1935909
WSDM
Keywords
Field
DocType
transient crowd discovery,social network,transient crowd,clustering approach,heavyweight static clustering algorithm,social messaging system,social information discovery,real-time social web,locality-based clustering approach result,evolution approach,highly-dynamic social messaging system,clustering,social media,real time,community structure,social web,near real time
Data mining,Crowds,Real-time web,Locality,Social media,Social network,Social web,Information retrieval,Computer science,Cluster analysis,Personalization
Conference
Citations 
PageRank 
References 
9
0.65
8
Authors
2
Name
Order
Citations
PageRank
Krishna Yeshwanth Kamath190.65
James Caverlee22484145.47