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Geo-Spatial Trend Detection Through Twitter Data Feed Mining

  • Maarten WijnantsEmail author
  • Adam Blazejczak
  • Peter Quax
  • Wim Lamotte
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 226)

Abstract

Present-day Social Networking Sites are steadily progressing towards becoming representative data providers. This paper proposes TweetPos, a versatile web-based tool that facilitates the analytical study of geographic tendencies in crowd-sourced Twitter data feeds. To accommodate the cognitive strengths of the human mind, TweetPos predominantly resorts to graphical data structures such as intensity maps and diagrams to visualize (geo-spatial) tweet metadata. The web service’s asset set encompasses a hybrid tweet compilation engine that allows for the investigation of both historic and real-time tweet posting attitudes, temporal trend highlighting via an integrated animation system, and a layered visualization scheme to support tweet topic differentiation. TweetPos ’ data mining features and the (geo-spatial) intelligence they can amount to are comprehensively demonstrated via the discussion of two representative use cases. Courtesy of its generic design, the TweetPos service might prove valuable to an interdisciplinary customer audience including social scientists and market analysts.

Keywords

Twitter Social networking sites Social media Geographic trends Investigative tool Data mining TweetPos 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maarten Wijnants
    • 1
    Email author
  • Adam Blazejczak
    • 1
  • Peter Quax
    • 1
  • Wim Lamotte
    • 1
  1. 1.Expertise Centre for Digital MediaHasselt University – tUL – iMindsDiepenbeekBelgium

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