@inproceedings{huang-cheng-2026-measuring-symbolic,
    title = "Measuring the Symbolic Power of Languages with {LLM}-based Multilingual Persuasion Simulation",
    author = "Huang, Yin Jou  and
      Cheng, Fei",
    editor = "Alves, Diego  and
      Bizzoni, Yuri  and
      Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Pagel, Janis  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 10th Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.latechclfl-1.32/",
    pages = "328--338",
    ISBN = "979-8-89176-373-9",
    abstract = "Prior studies on the symbolic power of languages have largely relied on surveys or localized experiments, limiting systematic comparison across cultures and domains. In this work, we propose an LLM-based multilingual persuasion simulation framework to quantify the symbolic power of languages through persuasion outcomes. We also introduce a Symbolic Power Index (SPI) that measures how language choice affects persuasion success and efficiency across domains. Experiments show that the LLM-based simulations largely reproduce established sociolinguistic prestige hierarchies tied to institutional authority and global power, especially in domains such as business, finance, education, and technology. These results suggest that LLM-based persuasion simulations offer a scalable, decision-making-driven approach to studying symbolic power in language."
}