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

2026-04-22

AI Summary

This study addresses the limitations of traditional metaphor analysis, which often focuses narrowly on source domains and struggles to uncover differences in the semantic frames activated by metaphors within complex discourses. To overcome this, the paper proposes an integrated framework that combines Conceptual Metaphor Theory with computational linguistics, leveraging natural language processing and semantic frame analysis to automatically identify salient metaphors in discourse and enable fine-grained cross-frame comparisons. Applied to climate news corpora, the method successfully detects both established and novel metaphorical frames. Furthermore, it reveals that while conservative and liberal media may employ the same source domains in discussions of immigration, they invoke markedly distinct semantic frames, thereby underscoring the constitutive role of metaphor in shaping political discourse.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Analogy
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• metaphorical framing
• source domains
• semantic frames
• discourse metaphors
• conceptual metaphor

Innovation

• metaphorical framing
• source domains
• semantic frames
• discourse metaphors
• computational linguistics