2020 ACL ACL 2020

Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness

Abstract

AbstractPredicting 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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — persuasion prediction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio