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Title:Stance classification of Twitter debates: The encryption debate as a use case
Author(s):Addawood, Aseel; Schneider, Jodi; Bashir, Masooda
Subject(s):Stance classification
Supervised machine learning
Natural language processing
Argumentative features
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.
Issue Date:2017-07-28
Publisher:ACM
Citation Info:Aseel Addawood, Jodi Schneider, Masooda Bashir, “Stance Classification of Twitter Debates: The Encryption Debate as A Use Case”, Proceedings of the International Conference on Social Media & Society (#SMSociety17), Toronto, Canada, July 28-30
Genre:Article
Conference Paper / Presentation
Type:Text
Language:English
URI:http://hdl.handle.net/2142/96250
DOI:https://doi.org/10.1145/3097286.3097288
Date Available in IDEALS:2017-06-08


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