# Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames

arXiv: https://arxiv.org/abs/2604.20454 · HTML: https://mlsanity.com/paper/2604.20454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 89 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Metaphor Generation with Conceptual Mappings](https://mlsanity.com/paper/2106.01228/) (2021)
- [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)
- [Long-Term Trends in the Public Perception of Artificial Intelligence](https://mlsanity.com/paper/1609.04904/) (2016)
- [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://mlsanity.com/paper/2302.04834/) (2023)
- [Coarse-to-Fine Dual Encoders are Better Frame Identification Learners](https://mlsanity.com/paper/2310.13316/) (2023)
- [Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding](https://mlsanity.com/paper/2507.15357/) (2025)
- [When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models](https://mlsanity.com/paper/2502.13246/) (2025)
- [How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection](https://mlsanity.com/paper/2104.03928/) (2021)
- [An Expectation-Realization Model for Metaphor Detection](https://mlsanity.com/paper/2311.03963/) (2023)
- [MetaphorShare: A Dynamic Collaborative Repository of Open Metaphor Datasets](https://mlsanity.com/paper/2411.18260/) (2024)

## Built on by (most cited first, 2 of 2)

- [Discovering Conceptual Metaphors Across Topics and Media Types](https://mlsanity.com/paper/2608.06652/) (2026)
- [The Multilingual FrameNet Corpus](https://mlsanity.com/paper/2608.23037/) (2026)

## Abstract (arXiv)

Metaphors are powerful framing devices, yet their source domains alone do not fully explain the specific associations they evoke. We argue that the interplay between source domains and semantic frames determines how metaphors shape understanding of complex issues, and present a computational framework that allows to derive salient discourse metaphors through their source domains and semantic frames. Applying this framework to climate change news, we uncover not only well-known source domains but also reveal nuanced frame-level associations that distinguish how the issue is portrayed. In analyzing immigration discourse across political ideologies, we demonstrate that liberals and conservatives systematically employ different semantic frames within the same source domains, with conservatives favoring frames emphasizing uncontrollability and liberals choosing neutral or more ``victimizing'' semantic frames. Our work bridges conceptual metaphor theory and linguistics, providing the first NLP approach for discovery of discourse metaphors and fine-grained analysis of differences in metaphorical framing. Code, data and statistical scripts are available at https://github.com/julia-nixie/ConceptFrameMet.