Result: Measuring Online Debaters’ Persuasive Skill from Text over Time

Title:
Measuring Online Debaters’ Persuasive Skill from Text over Time
Source:
Transactions of the Association for Computational Linguistics, Vol 7, Pp 537-550 (2019)
Publisher Information:
The MIT Press, 2019.
Publication Year:
2019
Collection:
LCC:Computational linguistics. Natural language processing
Document Type:
Academic journal article
File Description:
electronic resource
Language:
English
ISSN:
2307-387X
DOI:
10.1162/tacl_a_00281
Accession Number:
edsdoj.f13f7f31fec0438e8c860d7a0086678d
Database:
Directory of Open Access Journals

Further Information

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.