Abstract
To date, little attention has been paid to the temporal nature of campaigns as they respond to events or react to the different stages of a political election - what we define as strategic temporality. This article seeks to remedy this lack of research by examining campaign Facebook and Twitter messaging shifts during the 2016 U.S. Presidential general election. We used supervised machinelearning techniques to predict the types of messages that campaigns employed via social media and analyzed time-series data to identify messaging shifts over the course of the general election. We also examined how social media platforms and candidates' party affiliation shape campaign messaging. Results suggest differences exist in the types of campaign messages produced on different platforms during the general election. As election day drew closer, campaigns generated more calls-To-Action and informative messages on both Facebook and Twitter. This trend existed in advocacy campaign messages as well, but only on Twitter. Both advocacy and attack tweets were posted more frequently around Presidential and Vice-Presidential debate dates.
Original language | English (US) |
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Title of host publication | 8th International Conference on Social Media and Society |
Subtitle of host publication | Social Media for Good or Evil, #SMSociety 2017 |
Publisher | Association for Computing Machinery |
Volume | Part F129683 |
ISBN (Electronic) | 9781450348478 |
DOIs | |
State | Published - Jul 28 2017 |
Event | 8th International International Conference on Social Media and Society, #SMSociety 2017 - Toronto, Canada Duration: Jul 28 2017 → Jul 30 2017 |
Other
Other | 8th International International Conference on Social Media and Society, #SMSociety 2017 |
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Country | Canada |
City | Toronto |
Period | 7/28/17 → 7/30/17 |
Keywords
- Campaign messaging
- Campaign strategy
- Machine learning
- Political campaigns
- Social media
- Temporal trend.
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software