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Data mining locative thematic narratives: analysing twitter and POI data to extract precise spatial themes

Data mining locative thematic narratives: analysing twitter and POI data to extract precise spatial themes
Data mining locative thematic narratives: analysing twitter and POI data to extract precise spatial themes
This thesis produces a novel framework for constructing thematic narratives of space. Existing research into extracting information from Twitter data is fraught with inconsistencies and a general lack of attention to metadata. This thesis conducts five experiments to delve deeply into how to extract precise location-based themes from Twitter data, with a focus on under-researched metadata fields and exploration of external APIs to enrich the location data of previously assumed ‘precisely’ geolocated tweets. The analysis is based on Twitter data from June 2016 to August 2018. A key argument is made for the analysis of third-party sources and the stratification of tweet granularity to better understand the scale of location-based narratives. These experiments form a framework that is tested in a case study, then validated and evaluated with qualitative interviews with HM Treasury. This framework is the main contribution of the thesis.
University of Southampton
Bennett, Nicholas
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Bennett, Nicholas
7dc9ad84-3507-4595-9b0c-98c2a26be84e
Millard, David
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Martin, David
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Morley, Jeremy
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Bennett, Nicholas (2020) Data mining locative thematic narratives: analysing twitter and POI data to extract precise spatial themes. University of Southampton, Doctoral Thesis, 197pp.

Record type: Thesis (Doctoral)

Abstract

This thesis produces a novel framework for constructing thematic narratives of space. Existing research into extracting information from Twitter data is fraught with inconsistencies and a general lack of attention to metadata. This thesis conducts five experiments to delve deeply into how to extract precise location-based themes from Twitter data, with a focus on under-researched metadata fields and exploration of external APIs to enrich the location data of previously assumed ‘precisely’ geolocated tweets. The analysis is based on Twitter data from June 2016 to August 2018. A key argument is made for the analysis of third-party sources and the stratification of tweet granularity to better understand the scale of location-based narratives. These experiments form a framework that is tested in a case study, then validated and evaluated with qualitative interviews with HM Treasury. This framework is the main contribution of the thesis.

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More information

Published date: June 2020

Identifiers

Local EPrints ID: 448269
URI: http://eprints.soton.ac.uk/id/eprint/448269
PURE UUID: a4ef901f-b72a-46f2-9f82-0fded00cbc1c
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: 16 Apr 2021 16:34
Last modified: 13 Dec 2021 02:43

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Contributors

Author: Nicholas Bennett
Thesis advisor: David Millard ORCID iD
Thesis advisor: David Martin ORCID iD
Thesis advisor: Jeremy Morley

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