@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Toward Unsupervised Conceptual Metaphor Discovery: A Case Study in Online Immigration Discourse
%A Leto, Alexandria
%A Pacheco, Maria Leonor
%Y Card, Dallas
%Y Field, Anjalie
%Y Keith, Katherine
%Y Mendelsohn, Julia
%S Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego
%@ 979-8-89176-426-2
%F leto-pacheco-2026-toward
%X 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.
%U https://aclanthology.org/2026.nlpcss-1.11/
%P 159-175
Markdown (Informal)
[Toward Unsupervised Conceptual Metaphor Discovery: A Case Study in Online Immigration Discourse](https://aclanthology.org/2026.nlpcss-1.11/) (Leto & Pacheco, NLP+CSS 2026)
ACL