Twitter usage across industry: A spatiotemporal analysis

Neha Gupta, Henry Crosby, David Purser, Stephen Javis, Weisi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

High resolution social media data presents an opportunity to better understand people's behavioural patterns and sentiment. Whilst significant work has been conducted in various targeted social contexts, very little is understood about differentiated behaviour in different industrial sectors. In this paper, we present results on how social media usage and general sentiment vary across the geographic and industry sector landscape. Unlike existing studies, we use a novel geocomputational approach to link location specific Twitter data with business sectors by leveraging the UK Standard Industrial Classification Code (SIC Code). Our baseline results for the Greater London area identifies Construction, Real Estate, Transport and Financial Services industries consistently have stronger Twitter footprints. We go on to apply natural language processing (NLP) techniques to understand the prevailing sentiment within each business sector and discuss how the evidence can contribute towards de-biasing Twitter data. We believe this research will prove a valuable surveillance tool for policy makers and service providers to monitor ongoing sentiment in different industry sectors, perceive the impact of new policies and can be used as a low cost alternative to survey methods in organisational studies.

Original languageEnglish
Title of host publicationProceedings - IEEE 4th International Conference on Big Data Computing Service and Applications, BigDataService 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages64-71
Number of pages8
ISBN (Electronic)9781538651193
DOIs
Publication statusPublished - 5 Jul 2018
Externally publishedYes
Event4th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2018 - Bamberg, Germany
Duration: 26 Mar 201829 Mar 2018

Publication series

NameProceedings - IEEE 4th International Conference on Big Data Computing Service and Applications, BigDataService 2018

Conference

Conference4th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2018
Country/TerritoryGermany
CityBamberg
Period26/03/1829/03/18

Bibliographical note

Funding Information:
We thank Paul Davis from Assured Property Group for providing the access to Land Registry Commercial Ownership data to conduct this research. This study is funded by the EPSRC (Engineering and Physical Sciences Research Council) Centre for Doctoral Training in Urban Science under the research grant EP/L016400/1.

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Big Data Visualisation
  • GIS
  • Knowledge Integration
  • Social Media Analysis in industry

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Information Systems and Management
  • Communication

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