A Corpus for Modeling User and Language Effects in Argumentation on Online Debating

Esin Durmus, Claire Cardie


Abstract
Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of “user” traits — characteristics and beliefs of the participants — on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.
Anthology ID:
P19-1057
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
602–607
Language:
URL:
https://aclanthology.org/P19-1057
DOI:
10.18653/v1/P19-1057
Bibkey:
Cite (ACL):
Esin Durmus and Claire Cardie. 2019. A Corpus for Modeling User and Language Effects in Argumentation on Online Debating. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 602–607, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (Durmus & Cardie, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1057.pdf