Abstract
US domestic extremist groups have increased in number and are intensively utilizing the Internet as an effective tool to share resources and members with limited regard for geographic, legal, or other obstacles. Researchers find that monitoring extremist and hate groups’ web sites and analyzing their usage and content have become time consuming and challenging. In response, this chapter describes the development of automated or semiautomated methodologies for capturing, classifying, and organizing domestic extremist web site data and using them for analysis. We found that by analyzing the hyperlink structures and content of domestic extremist web sites and constructing social network maps, their interorganizational structure and cluster affinities could be identified. Such analysis results could help experts in terrorism, law enforcement, intelligence, and policy making domains better understand the domestic extremist phenomena and eventually boost our national security.
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Acknowledgments
This research has been supported in part by the following grants: (1) DHS/CNRI, “BorderSafe Initiative,” October 2003–March 2005, and (2) NSF/ITR, “COPLINK Center for Intelligence and Security Informatics – A Crime Data Mining Approach to Developing Border Safe Research,” EIA-0326348, September 2003–August 2005. We would like to thank all members of the Artificial Intelligence Lab at the University of Arizona who have contributed to the project, in particular Wei Xi, Feng Huang, Homa Atabakhsh, Cathy Larson, Chun-Ju Tseng, and Shing Ka Wu.
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Chen, H. (2012). US Domestic Extremist Groups. In: Dark Web. Integrated Series in Information Systems, vol 30. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1557-2_20
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DOI: https://doi.org/10.1007/978-1-4614-1557-2_20
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