Occupational representativeness in Twitter

Sunghwan Mac Kim, Stephen Wan, Cécile Paris

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

This paper describes an approach to detect one particular demographic characteristic, occupation (or profession) in Twitter user profiles. In this paper, we show how effective the approach is for estimating occupational population statistics in Australian Twitter by correlating them with real-world population obtained from 2011 Australian census data. We also demonstrate that we can gain more reliable social media insights in the context of occupational representativeness in Twitter if a non-standard occupation name is mapped into a standard occupation name. To our knowledge, this is the first attempt to build a machine learning model that automatically identifies linguistically noisy or open-ended occupations in Twitter, resulting in more reliable occupational population.

Original languageEnglish
Title of host publicationADCS 2016
Subtitle of host publicationProceedings of the 21st Australasian Document Computing Symposium
EditorsSarvnaz Karimi, Mark Carman
PublisherAssociation for Computing Machinery
Pages57-64
Number of pages8
ISBN (Electronic)9781450348652
DOIs
Publication statusPublished - 5 Dec 2016
Externally publishedYes
Event21st Australasian Document Computing Symposium, ADCS 2016 - Caulfield, Australia
Duration: 6 Dec 20167 Dec 2016

Other

Other21st Australasian Document Computing Symposium, ADCS 2016
CountryAustralia
CityCaulfield
Period6/12/167/12/16

Keywords

  • Twitter
  • occupation
  • census
  • Conditional Random Fields

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