AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments
Till Saenger, Musashi Hinck, Justin Grimmer, Brandon M. Stewart
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers1
Ask how each one uses itBuilds on2
Related papers
- Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual RequestsJiaao Chen, Diyi YangAAAI 2021 · 25 citations
- Persuading across Diverse Domains: a Dataset and Persuasion Large Language ModelChuhao Jin, Kening Ren, Lingzhen Kong, Xiting Wang et al.ACL 2024 · 9 citations
- ToMAP: Training Opponent-Aware LLM Persuaders with Theory of MindPeixuan Han, Zijia Liu, Jiaxuan YouICML 2026 · 9 citations
- HARGAN: Heterogeneous Argument Attention Network for Persuasiveness PredictionKuo Yu Huang, Hen-Hsen Huang, Hsin-Hsi ChenAAAI 2021 · 10 citations
- What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation ProcessJichuan Zeng, Jing Li, Yulan He, Cuiyun Gao et al.WWW 2020 · 16 citations
