Event detection and visualization for social text streams

Qiankun Zhao, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

35 Scopus citations


In this paper, we propose to detect events from social text streams by exploring the content as well as the temporal, and social dimensions. We define the term event in the social text streams(e.g., blogs, emails, and Usenets) as a set of relations between social actors on a specific topic over a certain time period. We represent social text streams as multi-graphs, where each node represents a social actor and each edge rep- resents a piece of text communication that connects two actors. The content and temporal associations within each text piece are embedded in the corresponding edge. Then, events are detected by combining text-based clustering, temporal segmentation, and graph cuts of social networks. Moreover, we provide a multi-dimensional visualization tool that visualizes the relations between different events along the three different dimensions. Experiments conducted with the Enron email dataset1 show the advantages of exploring the social and temporal dimensions along with content, and the usefulness of the visualization tool.

Original languageEnglish (US)
Title of host publicationICWSM 2007 - International Conference on Weblogs and Social Media
StatePublished - 2007
Event2007 International Conference on Weblogs and Social Media, ICWSM 2007 - Boulder, CO, United States
Duration: Mar 26 2007Mar 28 2007


Other2007 International Conference on Weblogs and Social Media, ICWSM 2007
Country/TerritoryUnited States
CityBoulder, CO

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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