Discovering Conceptual Metaphors Across Topics and Media Types

[2608.06652] Discovering Conceptual Metaphors Across Topics and Media Types Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2608.06652 (cs) [Submitted on 6 Aug 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: Discovering Conceptual Metaphors Across Topics and Media Types Authors: Alexandria Leto , Rohan Das , Juan Vásquez , Abram Handler , Maria Leonor Pacheco View a PDF of the paper titled Discovering Conceptual Metaphors Across Topics and Media Types, by Alexandria Leto and Rohan Das and Juan V\'asquez and Abram Handler and Maria Leonor Pacheco View PDF HTML (experimental) Abstract: Conceptual metaphors guide our thinking and actions by allowing us to reason about more abstract experiences (e.g., paying taxes) in terms of more concrete or embodied experiences (e.g., carrying a physical load) (Lakoff and Johnson, 2011). It follows that different conceptual metaphors can result in different reasoning: framing paying taxes as an investment in a community rather than a physical load leads to a very different outlook on taxation. Identifying the conceptual metaphors guiding a speaker or writer thus helps to reveal their framing of events. Though these metaphors can't be observed directly, groups of linguistic metaphors, metaphorical expressions as they appear in language, serve as evidence for them. Motivated by this, we present an unsupervised method that extracts linguistic metaphors from a corpus and uses a structured clustering approach to form groups corresponding to conceptual metaphors. Using this method, we point to key topical and framing differences in left- vs. right-leaning podcasts. For example, left-leaning podcasts tend to conceptualize media stories as a weapon, while right-leaning sources commonly discuss the economy as a system subject to vertical changes. Comments: 49 pages (8 main text), 8 figures Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.06652 [cs.CL] (or arXiv:2608.06652v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.06652 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alexandria Leto [ view email ] [v1] Thu, 6 Aug 2026 23:43:08 UTC (1,311 KB) [v2] Fri, 14 Aug 2026 21:01:43 UTC (1,311 KB) Full-text links: Access Paper: View a PDF of the paper titled Discovering Conceptual Metaphors Across Topics and Media Types, by Alexandria Leto and Rohan Das and Juan V\'asquez and Abram Handler and Maria Leonor Pacheco View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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