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The word embedding association test (WEAT) is an important method formeasuring linguistic biases against social groups such as ethnic minorities inlarge text corpora. It does so by comparing the semantic relatedness of wordsprototypical of the groups (e.g., names unique to those groups) and attributewords (e.g., 'pleasant' and 'unpleasant' words). We show that anti-black WEATestimates from geo-tagged social media data at the level of metropolitanstatistical areas strongly correlate with several measures of racialanimus--even when controlling for sociodemographic covariates. However, we alsoshow that every one of these correlations is explained by a third variable: thefrequency of Black names in the underlying corpora relative to White names.This occurs because word embeddings tend to group positive (negative) words andfrequent (rare) words together in the estimated semantic space. As thefrequency of Black names on social media is strongly correlated with BlackAmericans' prevalence in the population, this results in spurious anti-BlackWEAT estimates wherever few Black Americans live. This suggests that researchusing the WEAT to measure bias should consider term frequency, and alsodemonstrates the potential consequences of using black-box models like wordembeddings to study human cognition and behavior.

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency