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

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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Identifiers

ORCID for David Millard: orcid.org/0000-0002-7512-2710

ORCID for David Martin: orcid.org/0000-0003-0397-0769

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