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Proceedings Paper

Incremental visual text analytics of news story development
Author(s): Milos Krstajic; Mohammad Najm-Araghi; Florian Mansmann; Daniel A. Keim
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Paper Abstract

Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. Additionally, the stories have very complex relationships and characteristics that are difficult to model: they can be weakly or strongly connected, or they can merge or split over time. In this paper, we present a visual analytics system for exploration of news topics in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. We employ text clustering techniques to automatically extract stories from online news streams and present a visualization that: 1) shows temporal characteristics of stories in different time frames with different level of detail; 2) allows incremental updates of the display without recalculating the visual features of the past data; 3) sorts the stories by minimizing clutter and overlap from edge crossings. By using interaction, stories can be filtered based on their duration and characteristics in order to be explored in full detail with details on demand. To demonstrate the usefulness of our system, case studies with real news data are presented and show the capabilities for detailed dynamic text stream exploration.

Paper Details

Date Published: 25 January 2012
PDF: 12 pages
Proc. SPIE 8294, Visualization and Data Analysis 2012, 829407 (25 January 2012); doi: 10.1117/12.912456
Show Author Affiliations
Milos Krstajic, Univ. of Konstanz (Germany)
Mohammad Najm-Araghi, Univ. of Konstanz (Germany)
Florian Mansmann, Univ. of Konstanz (Germany)
Daniel A. Keim, Univ. of Konstanz (Germany)


Published in SPIE Proceedings Vol. 8294:
Visualization and Data Analysis 2012
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Robert Kosara; Mark A. Livingston; Jinah Park; Ian Roberts, Editor(s)

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