Blog Recommendation and Management Implications in an Emergency Context: An Information Entropy Perspective

Siqing Shan, Jihong Shi () and Qi Yan ()
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Siqing Shan: Department of Information System, School of Economics and Management, Beihang University, Beijing 100191, P. R. China
Jihong Shi: Department of Information System, School of Economics and Management, Beihang University, Beijing 100191, P. R. China
Qi Yan: Department of Information System, School of Economics and Management, Beihang University, Beijing 100191, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2017, vol. 34, issue 01, 1-22

Abstract: A modeling methodology for blog recommendation and forecasting based on information entropy is presented. With the increasing popularity of smartphones and the rapid development of the mobile Internet, the amount of user-generated content such as blogs is increasing daily. Valuable information, such as bloggers’ opinions, feelings, and attitudes, is often part of this content. Particularly in the context of an emergency, this information should also be used to facilitate decision making. The current blog recommendation model examines primarily users’ interests or content similarity, whereas in this paper, the value of the blog is considered. The primary contribution of this paper is the proposal of an information-entropy-based blog recommendation model for finding valuable blogs to facilitate decision-making in an emergency context. A series of indicators for evaluating a blog in an emergency context are proposed. Using the method of information entropy, a blog recommendation model is developed. The model can also be used to forecast the value of emergency blogs in the future. The model has been tested and validated using crawled data from the Sina Blog, and the results have demonstrated that the proposed model can effectively determine the value of emergency-related blogs.

Keywords: Blog recommendation; blog forecast; information entropy; risk management; emergency (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0217595917400073

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