# Discovering Conceptual Metaphors Across Topics and Media Types

arXiv: https://arxiv.org/abs/2608.06652 · HTML: https://mlsanity.com/paper/2608.06652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Framework for the Computational Linguistic Analysis of Dehumanization](https://mlsanity.com/paper/2003.03014/) (2020)
- [Metaphor Understanding Challenge Dataset for LLMs](https://mlsanity.com/paper/2403.11810/) (2024)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Large Language Model Displays Emergent Ability to Interpret Novel Literary Metaphors](https://mlsanity.com/paper/2308.01497/) (2023)
- [When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models](https://mlsanity.com/paper/2502.13246/) (2025)
- [A Dual-Perspective Metaphor Detection Framework Using Large Language Models](https://mlsanity.com/paper/2412.17332/) (2024)
- [Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames](https://mlsanity.com/paper/2604.20454/) (2026)
- [A Structured Clustering Approach for Inducing Media Narratives](https://mlsanity.com/paper/2604.10368/) (2026)

## Abstract (arXiv)

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.