Abstract
Twitter allows political candidates to broadcast messages directly to the public, some of which spread virally, potentially reaching new supportive audiences. During the 2014 U.S. gubernatorial election, 74 candidates for State Governor posted 20,580 tweets, of which 10,946 were retweeted almost 140,000 times. By analyzing a collection of tweets posted by gubernatorial candidates that were classified by machine learning into categories of message types, we find that while candidates tend to post tweets that advocate for themselves the most, the public is more likely to retweet attack messages and messages labeled as call-to-action. As measured by number of retweets, call-to-action tweets tend to reach the broadest audience. We also find that middle-level gatekeepers, those with between 1,800 and 26,000 followers, tend to have the most influence over the flow of political information. Since retweets tend to bring new followers, these findings suggest that politicians wishing to grow their audience may benefit from posting more call-to-action and attack messages, and that candidates may wish to find ways to actively enlist the support of middle-level gatekeepers.
Original language | English (US) |
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Pages (from-to) | 280-304 |
Number of pages | 25 |
Journal | Policy and Internet |
Volume | 11 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2019 |
Keywords
- curation logics
- diffusion
- information flow
- network gatekeepers
- political elections
ASJC Scopus subject areas
- Health(social science)
- Public Administration
- Health Policy
- Computer Science Applications