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

Preview	PDF WRAP-Twitter-across-industry-analysis-Gupta-2018.pdf - Accepted Version - Requires a PDF viewer. Download (2764Kb) | Preview
	PDF IEEE Big Data Services-2018.pdf - Published Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (2417Kb)

Request Changes to record.

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)
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
Divisions:	Faculty of 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:	Date Event 9 July 2018 Available 17 January 2018 Accepted
Date of first compliant deposit:	21 May 2018
DOI:	10.1109/BigDataService.2018.00018
Status:	Peer Reviewed
Publication Status:	Published
Publisher Statement:	© 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
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/L016400/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
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:	Organisation

Request changes or add full text files to a record

Repository staff actions (login required)

View Item