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Assessing Twitter geocoding resolution

Assessing Twitter geocoding resolution
Assessing Twitter geocoding resolution
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.
Twitter, census, POI, mapping, framework
ACM
Bennett, Nicholas
7dc9ad84-3507-4595-9b0c-98c2a26be84e
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Bennett, Nicholas
7dc9ad84-3507-4595-9b0c-98c2a26be84e
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f

Bennett, Nicholas, Millard, David and Martin, David (2018) Assessing Twitter geocoding resolution. In WebSci ’18, May 27–30, 2018, Amsterdam, Netherlands. ACM. 5 pp. (In Press)

Record type: Conference or Workshop Item (Paper)

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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More information

Accepted/In Press date: April 2018
Venue - Dates: 10th ACM Annual Conference on Web Science: Web Science 2018, Amsterdam, Netherlands, 2018-05-27 - 2018-05-30
Keywords: Twitter, census, POI, mapping, framework

Identifiers

Local EPrints ID: 420196
URI: https://eprints.soton.ac.uk/id/eprint/420196
PURE UUID: bad58796-4336-4551-91c3-3d5438c4dbb7
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 02 May 2018 16:30
Last modified: 06 Jul 2018 00:35

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