Speciesism refers to the different moral standing given to species, and includes human supremacy over animals, and differentiations between animals, where companion animals are given higher moral standing than other species. Although it is clear that speciesism is culturally encoded, psychological science has revealed more about manifestations of speciesism in individuals’ thought than in collective systems of meaning. We present a quantitative test of speciesism by applying machine-learning methods (word embeddings) to billions of English words derived from conversation, film, books, and the internet. We found evidence of anthropocentric speciesism: words denoting concern (vs. indifference) and value (vs. valuelessness) were more closely associated with words denoting humans compared to most other animals. We also found evidence of companion animal speciesism: the same words were more closely associated with words denoting companion animals compared to other animals. The work presents a large-scale quantitative account of speciesism in everyday language.

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