@inproceedings{mitran-etal-2025-probing,
    title = "Probing Narrative Morals: A New Character-Focused {MFT} Framework for Use with Large Language Models",
    author = "Mitran, Luca  and
      Wu, Sophie  and
      Piper, Andrew",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1449/",
    pages = "28502--28517",
    ISBN = "979-8-89176-332-6",
    abstract = "Moral Foundations Theory (MFT) provides a framework for categorizing different forms of moral reasoning, but its application to computational narrative analysis remains limited. We propose a novel character-centric method to quantify moral foundations in storytelling, using large language models (LLMs) and a novel Moral Foundations Character Action Questionnaire (MFCAQ) to evaluate the moral foundations supported by the behaviour of characters in stories. We validate our approach against human annotations and then apply it to a study of 2,697 folktales from 55 countries. Our findings reveal: (1) broad distribution of moral foundations across cultures, (2) significant cross-cultural consistency with some key regional differences, and (3) a more balanced distribution of positive and negative moral content than suggested by prior work. This work connects MFT and computational narrative analysis, demonstrating LLMs' potential for scalable moral reasoning in narratives."
}