Measuring Online Debaters’ Persuasive Skill from Text over Time

Kelvin Luu, Chenhao Tan, Noah A. Smith


Abstract
Online debates allow people to express their persuasive abilities and provide exciting opportunities for understanding persuasion. Prior studies have focused on studying persuasion in debate content, but without accounting for each debater’s history or exploring the progression of a debater’s persuasive ability. We study debater skill by modeling how participants progress over time in a collection of debates from Debate.org. We build on a widely used model of skill in two-player games and augment it with linguistic features of a debater’s content. We show that online debaters’ skill levels do tend to improve over time. Incorporating linguistic profiles leads to more robust skill estimation than winning records alone. Notably, we find that an interaction feature combining uncertainty cues (hedging) with terms strongly associated with either side of a particular debate (fightin’ words) is more predictive than either feature on its own, indicating the importance of fine- grained linguistic features.
Anthology ID:
Q19-1031
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
March
Year:
2019
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
537–550
Language:
URL:
https://www.aclweb.org/anthology/Q19-1031
DOI:
10.1162/tacl_a_00281
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PDF:
http://aclanthology.lst.uni-saarland.de/Q19-1031.pdf