Discovering Conceptual Metaphors Across Topics and Media Types

2026-08-06

AI Summary

This study addresses the automatic identification of implicit conceptual metaphors in language to uncover divergent cognitive framing across ideological stances. It proposes a novel unsupervised approach that, for the first time, integrates linguistic metaphor extraction with structured clustering to enable cross-topic and cross-media discovery of conceptual metaphors without requiring annotated data. Leveraging natural language processing techniques, the method extracts metaphorical expressions from raw corpora and abstracts them into higher-level conceptual metaphors. Applied to podcast transcripts from ideologically distinct media sources, the framework successfully reveals significant differences in issue framing: left-leaning outlets metaphorize news media as a “weapon,” whereas right-leaning sources conceptualize the economy as a “vertical change system.” These findings demonstrate the method’s effectiveness and analytical insight in cognitive framing research.

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.

Problem

• conceptual metaphors
• framing
• linguistic metaphors
• media analysis
• political discourse

Innovation

• conceptual metaphor
• unsupervised learning
• metaphor clustering
• framing analysis
• linguistic metaphor