Temporal and Information Flow Based Event Detection from Social Text Streams

Abstract

Recently, social text streams (e.g., blogs, web forums, and emails) have become ubiquitous with the evolution of the web. In some sense, social text streams are sen-sors of the real world. Often, it is desirable to extract real world events from the social text streams. How-ever, existing event detection research mainly focused only on the stream properties of social text streams but ignored the contextual, temporal, and social information embedded in the 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 as the information flow be-tween a group of 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 ac-tor and each edge represents the information flow be-tween two actors. The content and temporal associa-tions within the flow of information are embedded in the corresponding edge. Events are detected by combining text-based clustering, temporal segmentation, and infor-mation flow-based graph cuts of the dual graph of the social networks. Experiments conducted with the En-ron email dataset1 and the political blog dataset from Dailykos2 show the proposed event detection approach outperforms the other alternatives.