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

TL;DR

This paper introduces a computational framework to analyze how source domains and semantic frames jointly influence metaphorical framing in discourse, applied to climate change and immigration topics.

Contribution

It presents the first NLP approach for discovering discourse metaphors and analyzing differences in metaphorical framing across political ideologies.

Findings

Uncovered nuanced frame-level associations in climate change discourse.

Demonstrated systematic differences in semantic frames used by liberals and conservatives.

Provided a new computational method bridging metaphor theory and NLP.

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.