Geosemantic Network-of-Interest Construction Using Social Media Data

  • Sophia Karagiorgou
  • Dieter Pfoser
  • Dimitrios Skoutas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)


An ever increasing amount of geospatial data generated by mobile devices and social media applications becomes available and presents us with applications and also research challenges. The scope of this work is to discover persistent and meaningful knowledge from user-generated location-based “stories” as reported by Twitter data. We propose a novel methodology that converts geocoded tweets into a mixed geosemantic network-of-interest (NOI). It does so by introducing a novel network construction algorithm on segmented input data based on discovered mobility types. The generated network layers are then combined into a single network. This segmentation addresses also the challenges imposed by noisy, low-sampling rate “social media” trajectories. An experimental evaluation assesses the quality of the algorithms by constructing networks for London and New York. The results show that this method is robust and provides accurate and interesting results that allow us to discover transportation hubs and critical transportation infrastructure.


Road Network Transportation Network User Movement Social Medium Data Semantic Layer 
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 2014

Authors and Affiliations

  • Sophia Karagiorgou
    • 1
  • Dieter Pfoser
    • 2
  • Dimitrios Skoutas
    • 3
  1. 1.School of Rural and Surveying EngineeringNational Technical University of AthensGreece
  2. 2.Department of Geography and GeoInformation ScienceGeorge Mason UniversityUSA
  3. 3.Institute for the Management of Information Systems, R.C. ATHENAGreece

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