Visualizing streaming text data with dynamic graphs and maps

Abstract

The many endless rivers of text now available present a serious challenge in the task of gleaning, analyzing and discovering useful information. In this paper, we describe a methodology for visualizing text streams in real-time modeled as a dynamic graph and its derived map. The approach automatically groups similar messages into "countries," with keyword summaries, using semantic analysis, graph clustering and map generation techniques. It handles the need for visual stability across time by dynamic graph layout and Procrustes projection techniques, enhanced with a novel stable component packing algorithm. The result provides a continuous, succinct view of evolving topics of interest. To make these ideas concrete, we describe their application to an online service called TwitterScope. © 2013 Springer-Verlag.