Assessing Twitter Geocoding Resolution

ABSTRACT

User-defined location privacy settings on Twitter cause geolocated tweets to be placed at four different resolutions: precise, point of interest (POI), neighbourhood and city levels. The latter two levels are not described by Twitter or the API, resulting in a risk that clustered tweets are unintentionally treated as real clusters in spatial analyses. This paper outlines a framework to address these differing spatial resolutions and highlight the impact they can have on cartographic representations. As part of this framework this paper also outlines a method of discovering sources (third-party applications) that produce geolocated tweets but do not reflect genuine human activity. We found that including tweets at all spatial resolutions created an artificially inflated importance of certain locations within a city. Discovering device-level geocoded tweets was straight forward, but querying Foursquare's API was required to differentiate between neighbourhood level clusters and POIs.

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Index Terms

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing theory, concepts and paradigms

      • Social media