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Visualizing Streaming Text Data with Dynamic Graphs and Maps

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7704)

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.

Keywords

  • Delaunay Triangulation
  • Latent Dirichlet Allocation
  • Dynamic Graph
  • Visual Stability
  • Proximity Graph

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Gansner, E.R., Hu, Y., North, S. (2013). Visualizing Streaming Text Data with Dynamic Graphs and Maps. In: Didimo, W., Patrignani, M. (eds) Graph Drawing. GD 2012. Lecture Notes in Computer Science, vol 7704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36763-2_39

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  • DOI: https://doi.org/10.1007/978-3-642-36763-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36762-5

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