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[ARTICLE · art-89835] src=arxiv.org ↗ pub= topic=natural-language-processing verified=true sentiment=· neutral

Discovering Conceptual Metaphors Across Topics and Media Types

Researchers from an undisclosed institution present an unsupervised method that extracts linguistic metaphors from a corpus and uses structured clustering to identify conceptual metaphors, revealing framing differences across media types. Applying it to left- vs. right-leaning podcasts, they found that left-leaning podcasts conceptualize media stories as a weapon, while right-leaning sources discuss the economy as a system subject to vertical changes.

read1 min views1 publishedAug 10, 2026

arXiv:2608.06652v1 Announce Type: new 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.

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