12. CyberGate Visualization

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.