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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1576

Title: Visual analysis of terrorism events extracted from the public knowledge bases
Authors: Zhu, Weizhong
Chen, Chaomei
Keywords: Latent semantic indexing;Social network analysis;Storytelling
Issue Date: 17-Apr-2007
Publisher: Drexel University. College of Information Science and Technology.
Series/Report no.: IST Research Day 2007 posters
Abstract: Understanding the conceptual, temporal and social properties of terrorism is essential to unlock terrorists’ minds, track terrorists and make policies for emergency responses to terrorists’ attacks. The primary goal of this poster is to introduce an integrative approach to study the ideology of terrorism, social structures of terrorists’ organizations and consistent patterns of terrorism events hidden in the public knowledge bases. Our system, Storylines, provides a novel storytelling framework to examine our approach and to help user visually and interactively explore textual information from multiple resources with diverse perspectives. The system enables user study a body of unstructured text without prior knowledge of its thematic structure and automatically find Who, When, What and Where in a salient story. It integrates natural language processing, latent semantic indexing and social network analysis. Our system has been applied to the 2006 VAST contest data that is a synthesized dataset. The data sets of test cases we use for this poster also include a concentrated terrorism news resource ICT (http://www.ict.org.il/) and a web resource related to Cuban Missile Crisis.
URI: http://hdl.handle.net/1860/1576
Appears in Collections:Research Day Posters (IST)

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