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Cognitive Visualization of Popular Regions Discovered From Geo-Tagged Social Media Data

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  • Yunzhe Wang

    (The Hong Kong Polytechnic University, Kowloon, Hong Kong)

  • George Baciu

    (Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong)

  • Chenhui Li

    (The Hong Kong Polytechnic University, Kowloon, Hong Kong)

Abstract

This article focuses on the cognitive exploration of photo sharing data which contain information about the location where the photo was taken and potentially some description about the photo. Therefore, the features of photo-spots can be deduced. Spots with similar features constitute a region of cognitive interest. The objective is to identify these regions and allow users to explore into regions of interest by cognitive understanding of their features. The authors propose an approach that makes use of semantic analysis, data clustering, and cognitive visualization. In this article, the authors introduce the design of an interactive visualization interface which projects photo sharing data to cognitive social activity map components. The contributions are two-fold. First, the authors put forward a novel social-media data classification method. Second, the authors suggest a new method to explore social activity maps by discovering regions of cognitive interest. Experiments are performed on the Flickr dataset.

Suggested Citation

  • Yunzhe Wang & George Baciu & Chenhui Li, 2018. "Cognitive Visualization of Popular Regions Discovered From Geo-Tagged Social Media Data," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 12(1), pages 14-28, January.
    • Handle: RePEc:igg:jcini0:v:12:y:2018:i:1:p:14-28
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