Event Detection and Visualization for Social Text Streams

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 rela-tions 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 ac-tors. 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 visu-alizes 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 useful-ness of the visualization tool