Tweeting to the Target: Candidates’ Use of Strategic Messages and @Mentions on Twitter

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper reports on a mixed-methods (i.e., content analysis, machine learning, and quantitative analysis) study of Twitter use among 74 U.S. gubernatorial candidates during the 2014 election. In extending the theory of controlled interactivity, this article focuses on politicians’ use of the @mention where we detail differing messaging strategies when candidates mention themselves versus their opponents, and between incumbents and challengers. Results suggest that candidates use the @mention feature as a subtle audience targeting mechanism. Our work also offers a methodological contribution by showing that machine-learning models perform better when context variables are included.

Original languageEnglish (US)
Pages (from-to)3-18
Number of pages16
JournalJournal of Information Technology and Politics
Volume15
Issue number1
DOIs
StatePublished - Jan 2 2018

Keywords

  • analysis
  • machine learning
  • political elections
  • strategic messages
  • Twitter

ASJC Scopus subject areas

  • Computer Science(all)
  • Sociology and Political Science
  • Public Administration

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