While the Web has become a worldwide platform for communication, terrorists share their ideology and communicate with members on the "Dark Web"-the reverse side of the Web used by terrorists. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of terrorist activities hinder effective and efficient analysis of terrorist information on the Web. To improve understanding of terrorist activities, we have developed a novel methodology for collecting and analyzing Dark Web information. The methodology incorporates information collection, analysis, and visualization techniques, and exploits various Web information sources. We applied it to collecting and analyzing information of 39 Jihad Web sites and developed visualization of their site contents, relationships, and activity levels. An expert evaluation showed that the methodology is very useful and promising, having a high potential to assist in investigation and understanding of terrorist activities by producing results that could potentially help guide both policymaking and intelligence research.

Focuses on business firms in Singapore, identifying the industries in which the Internet is being used for business. These firms are early adopters in the local environment where use of the Internet for business is a new phenomenon still, and they provide information about their Internet experience in terms of their use, perceptions, and the problems encountered. Finds that companies in seven major industries lead in the business use of the Internet in Singapore: computer and information technology; hospitality; manufacturing; travel; retail; publications; and banking and finance. Most of the survey respondents use the Internet for marketing and advertising, customer service and support, information gathering, and, to a lesser degree, electronic transactions. The respondents’ perception of the attributes of the Internet are largely positive. The problems encountered by the respondents include difficulty in locating information, rising costs of Internet use, and security.

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This paper describes an exploratory, qualitative study of a process for extracting, identifying and exploiting an enterprise's implicit (less visible) web communities using link analysis. By identifying the implicit communities' relationships to a specific enterprise, the information can be organized into stakeholder groups and used for 'listening' to the external environments. A prototypology was derived using analysis of 445 hypertext links and their associated textual annotations (comments) to the web site of MicroStrategy, a business intelligence vendor. The proto-typology can be used not only to identify independent but implicit community of users who virtually interact and relate with an enterprise, but also understand how the enterprise supports their interests and value creation activities.

As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships. Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machinelearned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better withinclass accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.