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Integrated Visual Analysis of Global Terrorism

Explore trends in news content over time, discover breaking news, trace news development, retrieve news effectively, analyze multimedia content, compare group differences, and associate news with social events.

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Integrated Visual Analysis of Global Terrorism

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  1. Integrated Visual Analysis of Global Terrorism Remco Chang Charlotte Visualization Center UNC Charlotte

  2. Integrated Terrorism Analysis Multimedia Real Time Known Events Visual GTD

  3. Video Analysis Goals • to describetrends in news content over time • to discoverbreaking news and hot topics over time • to trace conceptual development of news • to retrievenews of interests effectively • to collect evidences and test hypotheses for intelligent analysis • to comparegroup (such as different channels) differences in content • to associatenews content with social events

  4. Multimedia Analysis

  5. Video Analysis Example CNN Fox News MSNBC • News contains view points and opinions • Find local, regional, national, and international reports of the same event to get a complete picture

  6. NVAC Collaborations • PNNL – A. Sanfilippo (Content Analysis and Information Extraction of closed caption) • PNNL – W. Pike (Emotional state extraction from closed caption) • Penn State – A. MacEachren (Geographical analysis) • Georgia Tech – J. Stasko (Jigsaw, entity relationships) • Visual Analytics is the point of integration!!

  7. Integrating Terrorism Data Analysisand News Analysis Terrorism Visual Analysis Terrorism Databases Terrorism VA Jigsaw NVAC Stab/ TIBOR Reasoning Environment Framing, Affective Analysis Broadcast VA News Visual Analysis News Story Databases Next: full, Web-based multimedia content

  8. Visual GTD Flow Chart Entity Relationships (Geo-temporal Vis) Dimensional Relationships (ParallelSets) Entity Analysis (Search By Example)

  9. WHO – Terrorist Groups Five Flexible Entry Components What WHERE~ WHEN

  10. Enter System by single or multiple Selections System will supply Specific Information Drilldown to Original Info

  11. Parallel Sets View • Parallel Sets • Displays relationships among categorical dimensions • Shows intersections and distributions of categories

  12. Parallel Sets View • Dynamic filtering on continuous dimensions can show more information • Here we see the large proportion of facility attacks and bombings in Latin America during the early 1980s

  13. Analysis using Longest Common Sequence (LCS) • Two strings of data (each representing a series of events) • GATCCAGT • GTACACTGAG • Basic algorithm returns length of longest common subsequence: 6 • Can return trace of subsequence if desired: • GTCCAG • GATCCAGT • GTACACTGAG • Additional variations can take into account event gap penalties, time gap penalties, and exploration of shorter, or alternate, common subsequences

  14. Grouping using MDS in 2D • Each o represents a terrorist group • Groups form cluster according to naturally occurring trend sizes • Sharp divide between large clusters in right hemisphere • Left hemisphere contains many smaller clusters MDS Analysis by TargetType

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