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