Understanding discourse acts: Political campaign messages classification on facebook and twitter

Feifei Zhang, Jennifer Stromer-Galley, Sikana Tanupabrungsun, Yatish Hegde, Nancy McCracken, Jeffrey Hemsley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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’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.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings
PublisherSpringer Verlag
Pages242-247
Number of pages6
Volume10354 LNCS
ISBN (Print)9783319602394
DOIs
StatePublished - 2017
Event10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017 - Washington, United States
Duration: Jul 5 2017Jul 8 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10354 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017
CountryUnited States
CityWashington
Period7/5/177/8/17

Keywords

  • Automated classification
  • Political campaign
  • Social media
  • Supervised learning
  • Text mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, F., Stromer-Galley, J., Tanupabrungsun, S., Hegde, Y., McCracken, N., & Hemsley, J. (2017). Understanding discourse acts: Political campaign messages classification on facebook and twitter. In Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings (Vol. 10354 LNCS, pp. 242-247). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10354 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-60240-0_29