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

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.