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Using Web Link Analysis to Detect and Analyze Hidden Web Communities

Using Web Link Analysis to Detect and Analyze Hidden Web Communities

Edna O.F. Reid
Copyright: © 2004 |Pages: 28
ISBN13: 9781591401421|ISBN10: 1591401429|ISBN13 Softcover: 9781591402145|EISBN13: 9781591401438
DOI: 10.4018/978-1-59140-142-1.ch003
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MLA

Reid, Edna O.F. "Using Web Link Analysis to Detect and Analyze Hidden Web Communities." Information and Communications Technology for Competitive Intelligence, edited by Dirk Vriens, IGI Global, 2004, pp. 57-84. https://doi.org/10.4018/978-1-59140-142-1.ch003

APA

Reid, E. O. (2004). Using Web Link Analysis to Detect and Analyze Hidden Web Communities. In D. Vriens (Ed.), Information and Communications Technology for Competitive Intelligence (pp. 57-84). IGI Global. https://doi.org/10.4018/978-1-59140-142-1.ch003

Chicago

Reid, Edna O.F. "Using Web Link Analysis to Detect and Analyze Hidden Web Communities." In Information and Communications Technology for Competitive Intelligence, edited by Dirk Vriens, 57-84. Hershey, PA: IGI Global, 2004. https://doi.org/10.4018/978-1-59140-142-1.ch003

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Abstract

A great deal of current as well as previous studies on web links has focused mostly on improving the performance of information retrieval systems. The vast but untapped wealth of information from link-related messages generated by online communities has yet to attract the attention of the competitive intelligence researchers and practitioners. The latter groups have depended mainly on traditional intelligence sources while cognizant that much of the information which impinges upon their competitive strengths is shaped by events external to the firm. In view of the foregoing, we present in this chapter an exploratory framework for extracting and exploiting patterns of self-organizing, hyperlinked web communities for corporate intelligence purposes. More specifically, this chapter summarizes how the proposed analytical framework has been applied to MicroStrategy Inc.’s website to give us a glimpse of stakeholder communities’ reactions to the enterprise’s activities and identify some early warning signals. The framework can thus be considered as a prototypical approach for exploiting the Web’s structure and content for Web intelligence purposes.

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