Waging metaphorical war: Cross-linguistic analysis of metaphors for cancer, COVID-19, and climate change
Why study metaphor? Metaphors are more than literary devices; they are deeply ingrained throughout our language. Moreover, metaphors operate on both a linguistic and a conceptual level: They influence how we reason about and understand the world around us, and in turn, the metaphors we use in our speech reflect our opinions, attitudes, and conceptual structures; they are a cognitive phenomenon, part of our “everyday rationality” (Dancygier & Sweetser 2014:41). For example, to say that we are in a “war” against COVID‑19 communicates that COVID‑19 is a metaphorical “enemy”, and that there is a conflict, meaning that damages can be done to both sides, and counter-attacks might occur. It might also suggest that if a person succumbs to the disease, they “lost their battle” because they didn’t fight hard enough, or that COVID‑19 is a conscious entity with a plan rather than a mindless virus.
Major challenges to systematically studying metaphor are the variety of its expression and the large amount of data required. Metaphor varies considerably cross-linguistically, which can be an impediment to translation and communication. The time-consuming nature of manual annotation “drastically limits the potential size of the corpus” (Stefanowitsch 2008:2). Therefore, cognitive linguistics is increasingly turning to corpus methods to complement traditional approaches (Bolognesi & Despot 2019). Corpus approaches frequently rely on lexicographic resources such as FrameNet (Ruppenhofer 2017), which provide the representations of the relations between words and concepts to perform semantically-informed searches. Simultaneously, interest in automated metaphor identification has grown in computational linguistics (Shutova 2015). Together these have raised the necessity of a resource for metaphors comparable to FrameNet. The answer to this critical need is MetaNet, an ontological repository of metaphors and semantic frames in English and Spanish, which is now widely used by computational and cognitive linguists. This project will undertake the first major cross-linguistic study using MetaNet and expand its scope and coverage to French.
We propose to analyze the metaphor systems for cancer, COVID-19, and climate change in five varieties of English, French, and Spanish across Canada, France, and the United States. We will develop a new MetaNet database for French and improve the existing English and Spanish data by expanding semantic coverage and documenting sociolinguistic variation. The extant databases are already valuable resources for metaphor research, but currently do not include analyses for these topics of global concern. Our team comprises six cognitive linguists in Canada, France, and the United States, representing the five language varieties. We have substantial prior experience researching these topics using techniques including discourse analysis, corpus linguistics, and computational methods. We will develop new tools for automatically identifying metaphoric language in very large text corpora, enabling the statistical analysis of metaphor frequency and distribution, and cross-linguistic comparisons.
Outcomes of this project include five publications and six presentations; enhanced computational metaphor analysis methods; and training for three undergraduates, and four graduate students. We will make publicly available the MetaNet databases in English, French, Spanish; custom corpora for studying COVID‑19 and cancer; and automated metaphor identification software for all three languages. Given the importance of our three areas of study and the need for metaphor resources, this expansion of the already-popular MetaNet repository in combination with the new corpora and software tools will become an important asset to metaphor research.
