On Spatial Measures of Geographic Relevance for Geotagged Social Media Content

  • Xin Wang
  • Tristan GaugelEmail author
  • Matthias Keller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9546)


Recently, geotagged social media contents became increasingly available to researchers and were subject to more and more studies. Different spatial measures such as Focus, Entropy and Spread have been applied to describe geospatial characteristics of social media contents. In this paper, we draw the attention to the fact that these popular measures do not necessarily show the geographic relevance or dependence of social content, but mix up geographic relevance, the distribution of the user population, and sample size. Therefore, results based on these measures cannot be interpreted as geographic effects alone. By means of an assessment, based on Twitter data collected over a time span of six weeks, we highlight potential misinterpretations and we furthermore propose normalized measures which show less dependency on the underlying user population and are able to mitigate the effect of outliers.


Unit Region Twitter User User Population Spatial Measure Spread Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Steinbuch Centre for Computing, Institute of TelematicsKarlsruhe Institute of TechnologyKarlsruheGermany

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