Detecting persuasive arguments based on author-reader personality traits and their interaction

Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, Tommy Sandbank

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Persuasion is one of the most frequent, albeit challenging, tasks in human interaction. In a textual argument, one party (author) aims to change the view of the other party (reader). In this paper, we propose to detect persuasive textual arguments while considering the parties personality traits. We find that we can substantially improve accuracy by introducing features that capture author-reader personality traits and their interaction. Our model improves performance of state-of-the-art baselines from 66% to 71% on a new dataset of more than 19K arguments we collected.

Original languageEnglish
Title of host publicationACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages211-215
Number of pages5
ISBN (Electronic)9781450360210
DOIs
StatePublished - 7 Jun 2019
Externally publishedYes
Event27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019 - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019

Publication series

NameACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019
Country/TerritoryCyprus
CityLarnaca
Period9/06/1912/06/19

Keywords

  • Personality traits
  • Persuasion
  • Social media

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