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

Addawood, Aseel; Schneider, Jodi; Bashir, Masooda

Permalink

https://hdl.handle.net/2142/96250 Copy

Description

Title

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

Author(s)

• Addawood, Aseel
• Schneider, Jodi
• Bashir, Masooda

Issue Date

2017-07-28

Keyword(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.

Publisher

ACM

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/96250

DOI

https://doi.org/10.1145/3097286.3097288

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