Stance classification of Twitter debates: The encryption debate as a use case

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

Abstract

Social media have enabled a revolution in user-generated content. They allow users to connect, build community, produce and share content, and publish opinions. To better understand online users' attitudes and opinions, we use stance classification. Stance classification is a relatively new and challenging approach to deepen opinion mining by classifying a user's stance in a debate. Our stance classification use case is tweets that were related to the spring 2016 debate over the FBI's request that Apple decrypt a user's iPhone. In this "encryption debate," public opinion was polarized between advocates for individual privacy and advocates for national security. We propose a machine learning approach to classify stance in the debate, and a topic classification that uses lexical, syntactic, Twitter-specific, and argumentative features as a predictor for classifications. Models trained on these feature sets showed significant increases in accuracy relative to the unigram baseline.

Original languageEnglish (US)
Title of host publication8th International Conference on Social Media and Society
Subtitle of host publicationSocial Media for Good or Evil, #SMSociety 2017
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450348478
DOIs
StatePublished - Jul 28 2017
Event8th International International Conference on Social Media and Society, #SMSociety 2017 - Toronto, Canada
Duration: Jul 28 2017Jul 30 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F129683

Other

Other8th International International Conference on Social Media and Society, #SMSociety 2017
CountryCanada
CityToronto
Period7/28/177/30/17

Keywords

  • Argumentative Features.
  • Natural Language Processing
  • Stance Classification
  • Supervised Machine Learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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