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Association between Web Semantics and Geographical Locations: Evaluate Internet Information Consistency for Marketing Research

Association between Web Semantics and Geographical Locations: Evaluate Internet Information Consistency for Marketing Research
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Author(s): Kimman Lui (The Hong Kong Polytechnic University, Hong Kong), Keith C.C. Chan (The Hong Kong Polytechnic University, Hong Kong)and Kai-Pan Mark (City University of Hong Kong, Hong Kong)
Copyright: 2009
Pages: 24
Source title: Handbook of Research on Social Dimensions of Semantic Technologies and Web Services
Source Author(s)/Editor(s): Maria Manuela Cruz-Cunha (Polytechnic Institute of Cavado and Ave, Portugal), Eva F. Oliveira (Polytechnic Institute of Cavado and Ave, Portugal), Antonio J. Tavares (Polytechnic Institute of Cavado and Ave, Portugal)and Luis G. Ferreira (Polytechnic Institute of Cavado and Ave, Portugal)
DOI: 10.4018/978-1-60566-650-1.ch008

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Abstract

Social software completely revolutionizes the way of information sharing by allowing every individual to read, share and publish online. In terms of marketing, it is an effective way to understand consumers’ perceptions and beliefs in different local regions by analyzing and comparing the web content regarding a specific product retrieved on the Internet with respect to different locations. Interestingly, incidents originated from a location may attract more Internet discussions by individuals from remote locations. Therefore, it is difficult to measure the strength of people’s perceptions between different locations if we solely rely on the web traffic statistics. Moreover, it is difficult to compare strength of perceptions retrieved by different search engines, at different times, and on different topics. To overcome these inadequacies, the authors introduce a quantitative metric, Perceived Index on Information (PI), to measure the strength of web content over different search engines, different time intervals, and different topics with respect to geographical locations. Further visualizing PI in maps provides an instant and low-cost mean for word-of-mouth analysis that brings competitive advantages in business marketing.

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