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Knowing the Tweeters: Deriving Sociologically Relevant Demographics from Twitter

Author

Listed:
  • Luke Sloan
  • Jeffrey Morgan
  • William Housley
  • Matthew Williams
  • Adam Edwards
  • Pete Burnap
  • Omer Rana

Abstract

A perennial criticism regarding the use of social media in social science research is the lack of demographic information associated with naturally occurring mediated data such as that produced by Twitter. However the fact that demographics information is not explicit does not mean that it is not implicitly present. Utilising the Cardiff Online Social Media ObServatory (COSMOS) this paper suggests various techniques for establishing or estimating demographic data from a sample of more than 113 million Twitter users collected during July 2012. We discuss in detail the methods that can be used for identifying gender and language and illustrate that the proportion of males and females using Twitter in the UK reflects the gender balance observed in the 2011 Census. We also expand on the three types of geographical information that can be derived from Tweets either directly or by proxy and how spatial information can be used to link social media with official curated data. Whilst we make no grand claims about the representative nature of Twitter users in relation to the wider UK population, the derivation of demographic data demonstrates the potential of new social media (NSM) for the social sciences. We consider this paper a clarion call and hope that other researchers test the methods we suggest and develop them further.

Suggested Citation

  • Luke Sloan & Jeffrey Morgan & William Housley & Matthew Williams & Adam Edwards & Pete Burnap & Omer Rana, 2013. "Knowing the Tweeters: Deriving Sociologically Relevant Demographics from Twitter," Sociological Research Online, , vol. 18(3), pages 74-84, August.
    • Handle: RePEc:sae:socres:v:18:y:2013:i:3:p:74-84
    DOI: 10.5153/sro.3001
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    Cited by:

    1. Carlo Corradini & Emma Folmer & Anna Rebmann, 2022. "Listening to the buzz: Exploring the link between firm creation and regional innovative atmosphere as reflected by social media," Environment and Planning A, , vol. 54(2), pages 347-369, March.
    2. P. N. Barbieri & F. Fazio & G. Gamberini, 2015. "Let Young People Join The Legislative Process. A Twitter Based Experiment On Internships," Working Papers wp995, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Nathan Wycoff & Lisa O. Singh & Ali Arab & Katharine M. Donato & Helge Marahrens, 2024. "The digital trail of Ukraine’s 2022 refugee exodus," Journal of Computational Social Science, Springer, vol. 7(2), pages 2147-2193, October.
    4. Tykhonov, Vyacheslav & van Leeuwen, Bas, 2021. "Regional sentiments in Covid tweets in the Netherlands before and during peak infections," MPRA Paper 110879, University Library of Munich, Germany.
    5. Nirmalya Thakur, 2022. "A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave," Data, MDPI, vol. 7(8), pages 1-16, August.
    6. Steve Kirkwood & Viviene Cree & Daniel Winterstein & Alex Nuttgens & Jenni Sneddon, 2018. "Encountering #Feminism on Twitter: Reflections on a Research Collaboration between Social Scientists and Computer Scientists," Sociological Research Online, , vol. 23(4), pages 763-779, December.
    7. Dilek Yildiz & Jo Munson & Agnese Vitali & Ramine Tinati & Jennifer A. Holland, 2017. "Using Twitter data for demographic research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(46), pages 1477-1514.
    8. Paul Chappell & Mike Tse & Minhao Zhang & Susan Moore, 2017. "Using GPS Geo-tagged Social Media Data and Geodemographics to Investigate Social Differences: A Twitter Pilot Study," Sociological Research Online, , vol. 22(3), pages 38-56, September.

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