@inproceedings{ad092c04276e4a7886641ad8e8d0eeab,
title = "Understanding discourse acts: Political campaign messages classification on facebook and twitter",
abstract = "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{\textquoteright}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.",
keywords = "Automated classification, Political campaign, Social media, Supervised learning, Text mining",
author = "Feifei Zhang and Jennifer Stromer-Galley and Sikana Tanupabrungsun and Yatish Hegde and Nancy McCracken and Jeff Hemsley",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017 ; Conference date: 05-07-2017 Through 08-07-2017",
year = "2017",
doi = "10.1007/978-3-319-60240-0_29",
language = "English (US)",
isbn = "9783319602394",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "242--247",
editor = "Nathaniel Osgood and Dongwon Lee and Robert Thomson and Yu-Ru Lin",
booktitle = "Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings",
}