TY - GEN
T1 - Understanding discourse acts
T2 - 10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017
AU - Zhang, Feifei
AU - Stromer-Galley, Jennifer
AU - Tanupabrungsun, Sikana
AU - Hegde, Yatish
AU - McCracken, Nancy
AU - Hemsley, Jeff
PY - 2017
Y1 - 2017
N2 - To understand political campaign messages in depth, we developed automated classification models for classifying categories of political campaign Twitter and Facebook messages, such as calls-to-action and persuasive messages. We used 2014 U.S. governor’s campaign social media messages to develop models, then tested these models on a randomly selected 2016 U.S. presidential campaign social media dataset. Our classifiers reach.75 micro-averaged F value on training sets and.76 micro-averaged F value on test sets, suggesting that the models can be applied to classify English-language political campaign social media messages. Our study also suggests that features afforded by social media help improve classification performance in social media documents.
AB - To understand political campaign messages in depth, we developed automated classification models for classifying categories of political campaign Twitter and Facebook messages, such as calls-to-action and persuasive messages. We used 2014 U.S. governor’s campaign social media messages to develop models, then tested these models on a randomly selected 2016 U.S. presidential campaign social media dataset. Our classifiers reach.75 micro-averaged F value on training sets and.76 micro-averaged F value on test sets, suggesting that the models can be applied to classify English-language political campaign social media messages. Our study also suggests that features afforded by social media help improve classification performance in social media documents.
KW - Automated classification
KW - Political campaign
KW - Social media
KW - Supervised learning
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85022340713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022340713&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-60240-0_29
DO - 10.1007/978-3-319-60240-0_29
M3 - Conference contribution
AN - SCOPUS:85022340713
SN - 9783319602394
VL - 10354 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 242
EP - 247
BT - Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings
PB - Springer Verlag
Y2 - 5 July 2017 through 8 July 2017
ER -