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Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data

Sloan, Luke ORCID:, Morgan, Jeffrey, Burnap, Pete ORCID: and Williams, Matthew ORCID: 2015. Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS ONE 10 (3) , e0115545. 10.1371/journal.pone.0115545

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This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Social Sciences (Includes Criminology and Education)
Cardiff Centre for Crime, Law and Justice (CCLJ)
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Computer software
Additional Information: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Publisher: Public Library of Science
ISSN: 1932-6203
Funders: ESRC & JISC
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 25 November 2014
Last Modified: 04 May 2023 08:16

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