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Dark Web pp 227-256 | Cite as

CyberGate Visualization

  • Hsinchun ChenEmail author
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 30)

Abstract

Computer-mediated communication (CMC) analysis systems are important for improving participant accountability and researcher analysis capabilities. However, existing CMC systems focus on structural features, with little support for analysis of text content in web discourse. In order to address this shortcoming, we propose a framework for CMC text analysis grounded in Systemic Functional Linguistic Theory. Our framework addresses several ambiguous CMC text mining issues, including the relevant tasks, features, information types, feature selection methods, and visualization techniques. Based on it, we have developed a system called CyberGate, which includes the Writeprint and Ink Blot techniques. These techniques incorporate complementary feature selection and visualization methods in order to allow a breadth of analysis and categorization capabilities. An application example is used to illustrate the ability of these techniques for CMC text analysis. Furthermore, experiments were conducted in comparison with a benchmark technique (Support Vector Machine) in order to assess the viability of CyberGate’s Writeprint and Ink Blot techniques for categorization of various forms of CMC text. The results indicated that the CyberGate techniques matched the Support Vector Machine performance in most cases while outperforming it for certain information types. Collectively, the results indicate that the system and its underlying design framework can dramatically improve text content analysis functions over those found in existing CMC systems.

Keywords

Feature Selection Text Analysis Noun Phrase Text Mining Feature Selection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research has been supported in part by the following grant: National Science Foundation (NSF), “COPLINK Center: Social Network Analysis and Identity Deception Detection for Law Enforcement and Homeland Security,” October 2004–September 2007.

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Authors and Affiliations

  1. 1.Department of Management Information SystemsUniversity of ArizonaTusconUSA

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