@inproceedings{leto-pacheco-2026-toward,
    title = "Toward Unsupervised Conceptual Metaphor Discovery: A Case Study in Online Immigration Discourse",
    author = "Leto, Alexandria  and
      Pacheco, Maria Leonor",
    editor = "Card, Dallas  and
      Field, Anjalie  and
      Keith, Katherine  and
      Mendelsohn, Julia",
    booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
    month = jul,
    year = "2026",
    address = "San Diego",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlpcss-1.11/",
    pages = "159--175",
    ISBN = "979-8-89176-426-2",
    abstract = "In Conceptual Metaphor Theory (CMT), a metaphor is a systematic mapping from a concrete source domain (e.g., physical load) to a more abstract target domain (e.g., taxes), so that reasoning about concepts in the target domain is guided by inferences from the source domain. In this work, we propose that since different source domains can frame the same target in starkly different ways, the conceptual mappings evidenced by metaphorical expressions can guide computational political discourse analysis. We present a proof-of-concept for an unsupervised method that uncovers salient conceptual mappings from a corpus. Prior work in computational political metaphor analysis has drawn on CMT, but it typically requires a predetermined inventory of focused source and target domains. In contrast, we introduce a simple LLM-based method that detects metaphorical expressions from a corpus with strong performance, then clusters them to approximate source domain categories. We demonstrate its utility through a case study on online immigration discourse, showing that the resulting metaphor clusters provide context for frame analysis. We conclude by outlining future work needed to develop a robust framework for conceptual metaphor discovery in political discourse."
}