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 language | English (US) |
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Pages (from-to) | 3-18 |
Number of pages | 16 |
Journal | Journal of Information Technology and Politics |
Volume | 15 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2 2018 |
Keywords
- analysis
- machine learning
- political elections
- strategic messages
ASJC Scopus subject areas
- Computer Science(all)
- Sociology and Political Science
- Public Administration