TY - GEN
T1 - Strategic temporality on social media during the general election of the 2016 u.s. presidential campaign
AU - Zhang, Feifei
AU - Robinson, Jerry Lamont
AU - Stromer-Galley, Jennifer
AU - Tanupabrungsun, Sikana
AU - Semaan, Bryan
AU - Boichak, Olga
AU - Hemsley, Jeff
AU - Bryant, Lauren
AU - Hegde, Yatish
N1 - Funding Information:
This project was supported in part by the Tow Center for Digital Journalism at Columbia University and the Center for Computational and Data Sciences at the School of Information Studies at Syracuse University.
Publisher Copyright:
© 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - 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.
AB - 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.
KW - Campaign messaging
KW - Campaign strategy
KW - Machine learning
KW - Political campaigns
KW - Social media
KW - Temporal trend.
UR - http://www.scopus.com/inward/record.url?scp=85028733269&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028733269&partnerID=8YFLogxK
U2 - 10.1145/3097286.3097311
DO - 10.1145/3097286.3097311
M3 - Conference contribution
AN - SCOPUS:85028733269
T3 - ACM International Conference Proceeding Series
BT - 8th International Conference on Social Media and Society
PB - Association for Computing Machinery
T2 - 8th International International Conference on Social Media and Society, #SMSociety 2017
Y2 - 28 July 2017 through 30 July 2017
ER -