Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness

Khalid Al Khatib, Michael Völske, Shahbaz Syed, Nikolay Kolyada, Benno Stein


Abstract
Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising. While clearly relevant to the task, the personal characteristics of an argument’s source and audience have not yet been fully exploited toward automated persuasiveness prediction. In this paper, we model debaters’ prior beliefs, interests, and personality traits based on their previous activity, without dependence on explicit user profiles or questionnaires. Using a dataset of over 60,000 argumentative discussions, comprising more than three million individual posts collected from the subreddit r/ChangeMyView, we demonstrate that our modeling of debater’s characteristics enhances the prediction of argument persuasiveness as well as of debaters’ resistance to persuasion.
Anthology ID:
2020.acl-main.632
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7067–7072
Language:
URL:
https://aclanthology.org/2020.acl-main.632
DOI:
10.18653/v1/2020.acl-main.632
Bibkey:
Cite (ACL):
Khalid Al Khatib, Michael Völske, Shahbaz Syed, Nikolay Kolyada, and Benno Stein. 2020. Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7067–7072, Online. Association for Computational Linguistics.
Cite (Informal):
Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness (Al Khatib et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.632.pdf
Video:
 http://slideslive.com/38929405