Speciesism, like other forms of prejudice, is thought to be underpinned by biased patterns of language use. Thus far, however, psychological science has primarily focused on how speciesism is reflected in individuals' thoughts as opposed to wider collective systems of meaning such as language. We present a large-scale 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. valueless) were more closely associated with words denoting humans compared to many other animals. We also found evidence of companion animal speciesism: the same words were more closely associated with words denoting companion animals compared to most other animals. The work describes speciesism as a pervasive collective phenomenon that is evident in a naturally occurring expression of human psychology – everyday language.

中文翻译：

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日常语言中的物种歧视

与其他形式的偏见一样，物种歧视被认为是由有偏见的语言使用模式支撑的。然而，到目前为止，心理科学主要关注物种歧视如何反映在个人思想中，而不是更广泛的集体意义系统，如语言。我们通过将机器学习方法（词嵌入）应用于来自对话、电影、书籍和互联网的数十亿个英语单词，对物种歧视进行了大规模的定量测试。我们发现了以人类为中心的物种歧视的证据：与许多其他动物相比，表示关注（与冷漠）和价值（与无价值）的词与表示人类的词更密切相关。我们还发现了伴侣动物物种歧视的证据：与大多数其他动物相比，相同的词与表示伴侣动物的词更密切相关。该作品将物种歧视描述为一种普遍存在的集体现象，这种现象在人类心理的自然表达——日常语言中是显而易见的。