@inproceedings{bhatti-etal-2021-argument,
title = "Argument Mining on {T}witter: A Case Study on the Planned Parenthood Debate",
author = "Bhatti, Muhammad Mahad Afzal and
Ahmad, Ahsan Suheer and
Park, Joonsuk",
editor = "Al-Khatib, Khalid and
Hou, Yufang and
Stede, Manfred",
booktitle = "Proceedings of the 8th Workshop on Argument Mining",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.argmining-1.1",
doi = "10.18653/v1/2021.argmining-1.1",
pages = "1--11",
abstract = "Twitter is a popular platform to share opinions and claims, which may be accompanied by the underlying rationale. Such information can be invaluable to policy makers, marketers and social scientists, to name a few. However, the effort to mine arguments on Twitter has been limited, mainly because a tweet is typically too short to contain an argument {---} both a claim and a premise. In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest. To demonstrate the efficacy of this formulation, we mine arguments for and against funding Planned Parenthood expressed in tweets. We first present a new dataset of 24,100 tweets containing hashtag {\#}StandWithPP or {\#}DefundPP, manually labeled as SUPPORT WITH REASON, SUPPORT WITHOUT REASON, and NO EXPLICIT SUPPORT. We then train classifiers to determine the types of tweets, achieving the best performance of 71{\%} F1. Our results manifest claim-specific keywords as the most informative features, which in turn reveal prominent arguments for and against funding Planned Parenthood.",
}
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<abstract>Twitter is a popular platform to share opinions and claims, which may be accompanied by the underlying rationale. Such information can be invaluable to policy makers, marketers and social scientists, to name a few. However, the effort to mine arguments on Twitter has been limited, mainly because a tweet is typically too short to contain an argument — both a claim and a premise. In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest. To demonstrate the efficacy of this formulation, we mine arguments for and against funding Planned Parenthood expressed in tweets. We first present a new dataset of 24,100 tweets containing hashtag #StandWithPP or #DefundPP, manually labeled as SUPPORT WITH REASON, SUPPORT WITHOUT REASON, and NO EXPLICIT SUPPORT. We then train classifiers to determine the types of tweets, achieving the best performance of 71% F1. Our results manifest claim-specific keywords as the most informative features, which in turn reveal prominent arguments for and against funding Planned Parenthood.</abstract>
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%0 Conference Proceedings
%T Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate
%A Bhatti, Muhammad Mahad Afzal
%A Ahmad, Ahsan Suheer
%A Park, Joonsuk
%Y Al-Khatib, Khalid
%Y Hou, Yufang
%Y Stede, Manfred
%S Proceedings of the 8th Workshop on Argument Mining
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F bhatti-etal-2021-argument
%X Twitter is a popular platform to share opinions and claims, which may be accompanied by the underlying rationale. Such information can be invaluable to policy makers, marketers and social scientists, to name a few. However, the effort to mine arguments on Twitter has been limited, mainly because a tweet is typically too short to contain an argument — both a claim and a premise. In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest. To demonstrate the efficacy of this formulation, we mine arguments for and against funding Planned Parenthood expressed in tweets. We first present a new dataset of 24,100 tweets containing hashtag #StandWithPP or #DefundPP, manually labeled as SUPPORT WITH REASON, SUPPORT WITHOUT REASON, and NO EXPLICIT SUPPORT. We then train classifiers to determine the types of tweets, achieving the best performance of 71% F1. Our results manifest claim-specific keywords as the most informative features, which in turn reveal prominent arguments for and against funding Planned Parenthood.
%R 10.18653/v1/2021.argmining-1.1
%U https://aclanthology.org/2021.argmining-1.1
%U https://doi.org/10.18653/v1/2021.argmining-1.1
%P 1-11
Markdown (Informal)
[Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate](https://aclanthology.org/2021.argmining-1.1) (Bhatti et al., ArgMining 2021)
ACL