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Research on the application of modeling techniques to the study of emergent behavior of VNSAs in cyberspace is ongoing. As evidenced by some of the results discussed above, there is room for significant progress to be made in this field. The University of Arizona group responsible for Deterring VNSA in Cyberspace the Dark Web collection continues to analyze the data from several different perspectives.
Forthcoming publications include a study of “sentiment and affect” analysis of Web content.
The analysis allows them to quantify the levels of radical sentiment and violence in Web content with the goal of identifying sites that warrant further investigation (Univ. of Arizona, 2008). The study also examines the process by which “ideas become infectious based on their contents,” and the group implemented visualization techniques that can be used to monitor the change in sentiment and affect over time among a group of people.
The continued growth of clean, content-rich raw data sources, such as the Dark Web collection, is critical for the further development of modeling techniques, as is the development of information portals that provide efficient access to the data. The Dark Web Portal is an example of a database access tool that utilizes document summarization, categorization, and visualization techniques to allow users to quickly locate, browse, and analyze the multilingual information they seek (Zhou et al, 2005).
Given the magnitude of the Dark Web collection, an access tool such as the Portal is a necessity, and work is ongoing to include additional natural language processing techniques such as entity and relations extraction to improve its ability to interpret user search commands. The further development of multilingual techniques for the classification of Web content is also a critical area of research, particularly for Arabic Web content. The ontology of the Arabic language poses significant challenges for classification techniques that are based on an English phenomenology, so continued development of language-specific techniques, such as those employed by the Dark Web group in the authorship identification study described above, are needed.
Given the interdisciplinary nature of the modeling process, collaborative tools like NEMESIS are particularly useful in the counter-terrorism domain. Having a platform that incorporates multiple modeling methodologies (such as ASAM and ORA) that can combine inputs from multiple users is an invaluable tool, and further development of these types of collaborative tools can help make predictive models more effective.
It is the advancement of techniques for the simulation of counter-terrorism measures, however, that could have the largest impact on the use of predictive models for decision support. Further development of this application can help realize one of the main goals of predictive modeling: to provide analysts with the ability to accurately predict the outcome of multiple counter-terrorism strategies before selecting a course of corporeal action. Overall, it is evident from the current status of this field of research that when used responsibly and with a clear understanding of their limitations, data mining and predictive modeling techniques have the potential to be powerful counter-terrorism weapons, particularly as tools that may be applied to better understand and predict the behavior of violent non-state actors.
Deterring VNSA in Cyberspace
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