Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames
This work addresses the need for fine-grained analysis of metaphorical framing in discourse, bridging conceptual metaphor theory and linguistics with a novel NLP approach.
The authors tackled the problem of understanding how metaphors frame complex issues by developing a computational framework that analyzes source domains and semantic frames, revealing nuanced associations in climate change news and systematic differences in metaphorical framing between liberals and conservatives in immigration discourse.
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.