AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments

Till Raphael Saenger, Musashi Hinck, Justin Grimmer, Brandon M. Stewart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce AutoPersuade, a three-part framework for constructing persuasive messages. First, we curate a large dataset of arguments with human evaluations. Next, we develop a novel topic model to identify argument features that influence persuasiveness. Finally, we use this model to predict the effectiveness of new arguments and assess the causal impact of different components to provide explanations. We validate AutoPersuade through an experimental study on arguments for veganism, demonstrating its effectiveness with human studies and out-of-sample predictions.

Original languageEnglish (US)
Title of host publicationEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
EditorsYaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
PublisherAssociation for Computational Linguistics (ACL)
Pages16325-16342
Number of pages18
ISBN (Electronic)9798891761643
DOIs
StatePublished - 2024
Event2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024 - Hybrid, Miami, United States
Duration: Nov 12 2024Nov 16 2024

Publication series

NameEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
Country/TerritoryUnited States
CityHybrid, Miami
Period11/12/2411/16/24

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Linguistics and Language

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