TY - GEN
T1 - "What is Your Evidence?" A Study of Controversial Topics on Social Media
AU - Addawood, Aseel A.
AU - Bashir, Masooda N.
N1 - Publisher Copyright:
© 2016 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All rights reserved.
PY - 2016
Y1 - 2016
N2 - In recent years, social media has revolutionized how people communicate and share information. One function of social media, besides connecting with friends, is sharing opinions with others. Micro blogging sites, like Twitter, have often provided an online forum for social activism. When users debate about controversial topics on social media, they typically share different types of evidence to support their claims. Classifying these types of evidence can provide an estimate for how adequately the arguments have been supported. We first introduce a manually built gold standard dataset of 3000 tweets related to the recent FBI and Apple encryption debate. We develop a framework for automatically classifying six evidence types typically used on Twitter to discuss the debate. Our findings show that a Support Vector Machine (SVM) classifier trained with n-gram and additional features is capable of capturing the different forms of representing evidence on Twitter, and exhibits significant improvements over the unigram baseline, achieving a F1 macroaveraged of 82.8%.
AB - In recent years, social media has revolutionized how people communicate and share information. One function of social media, besides connecting with friends, is sharing opinions with others. Micro blogging sites, like Twitter, have often provided an online forum for social activism. When users debate about controversial topics on social media, they typically share different types of evidence to support their claims. Classifying these types of evidence can provide an estimate for how adequately the arguments have been supported. We first introduce a manually built gold standard dataset of 3000 tweets related to the recent FBI and Apple encryption debate. We develop a framework for automatically classifying six evidence types typically used on Twitter to discuss the debate. Our findings show that a Support Vector Machine (SVM) classifier trained with n-gram and additional features is capable of capturing the different forms of representing evidence on Twitter, and exhibits significant improvements over the unigram baseline, achieving a F1 macroaveraged of 82.8%.
UR - http://www.scopus.com/inward/record.url?scp=85049409040&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85049409040
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 1
EP - 11
BT - ACL 2016 - 54th Annual Meeting of the Association for Computational Linguistics, Proceedings of the 3rd Workshop on Argument Mining, ArgMining 2016
A2 - Reed, Chris
PB - Association for Computational Linguistics (ACL)
T2 - 3rd Workshop on Argument Mining, ArgMining 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Y2 - 12 August 2016 through 12 August 2016
ER -