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Twitter usage across industry : a spatiotemporal analysis
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Gupta, Neha, Crosby, Henry James, Purser, David, Jarvis, Stephen A. and Guo, Weisi (2018) Twitter usage across industry : a spatiotemporal analysis. In: 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications, Bamberg, Germany, 26-29 Mar 2018 . Published in: 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService) ISBN 9781538651193. doi:10.1109/BigDataService.2018.00018
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Official URL: https://doi.org/10.1109/BigDataService.2018.00018
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.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Twitter (Firm), Social media, Big data, Industrial management | ||||||
Journal or Publication Title: | 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService) | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781538651193 | ||||||
Official Date: | 9 July 2018 | ||||||
Dates: |
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DOI: | 10.1109/BigDataService.2018.00018 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 21 May 2018 | ||||||
Date of first compliant Open Access: | 15 November 2018 | ||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||
Title of Event: | 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Bamberg, Germany | ||||||
Date(s) of Event: | 26-29 Mar 2018 | ||||||
Related URLs: |
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