TY - GEN
T1 - Stance classification of twitter debates
T2 - 8th International International Conference on Social Media and Society, #SMSociety 2017
AU - Addawood, Aseel
AU - Schneider, Jodi
AU - Bashir, Masooda
N1 - Publisher Copyright:
© 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - 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.
AB - 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.
KW - Argumentative Features.
KW - Natural Language Processing
KW - Stance Classification
KW - Supervised Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85028717997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028717997&partnerID=8YFLogxK
U2 - 10.1145/3097286.3097288
DO - 10.1145/3097286.3097288
M3 - Conference contribution
AN - SCOPUS:85028717997
T3 - ACM International Conference Proceeding Series
BT - 8th International Conference on Social Media and Society
PB - Association for Computing Machinery
Y2 - 28 July 2017 through 30 July 2017
ER -