@inproceedings{saenger-etal-2024-autopersuade,
    title = "{A}uto{P}ersuade: A Framework for Evaluating and Explaining Persuasive Arguments",
    author = "Saenger, Till Raphael  and
      Hinck, Musashi  and
      Grimmer, Justin  and
      Stewart, Brandon M.",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.913/",
    doi = "10.18653/v1/2024.emnlp-main.913",
    pages = "16325--16342",
    abstract = "We introduce a three-part framework for constructing persuasive messages, AutoPersuade. First, we curate a large collection of arguments and gather human evaluations of their persuasiveness. Next, we introduce a novel topic model to identify the features of these arguments that influence persuasion. Finally, we use the model to predict the persuasiveness of new arguments and to assess the causal effects of argument components, offering an explanation of the results. We demonstrate the effectiveness of AutoPersuade in an experimental study on arguments for veganism, validating our findings through human studies and out-of-sample predictions."
}