AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments
October 11, 2024 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Till Raphael Saenger, Musashi Hinck, Justin Grimmer, Brandon M. Stewart
arXiv ID
2410.08917
Category
cs.CL: Computation & Language
Citations
4
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
5 months ago
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.
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