  Dogwhistles are coded expressions that simultaneously convey one meaning to a
broad audience and a second one, often hateful or provocative, to a narrow
in-group; they are deployed to evade both political repercussions and
algorithmic content moderation. For example, in the sentence 'we need to end
the cosmopolitan experiment,' the word 'cosmopolitan' likely means 'worldly' to
many, but secretly means 'Jewish' to a select few. We present the first
large-scale computational investigation of dogwhistles. We develop a typology
of dogwhistles, curate the largest-to-date glossary of over 300 dogwhistles
with rich contextual information and examples, and analyze their usage in
historical U.S. politicians' speeches. We then assess whether a large language
model (GPT-3) can identify dogwhistles and their meanings, and find that
GPT-3's performance varies widely across types of dogwhistles and targeted
groups. Finally, we show that harmful content containing dogwhistles avoids
toxicity detection, highlighting online risks of such coded language. This work
sheds light on the theoretical and applied importance of dogwhistles in both
NLP and computational social science, and provides resources for future
research in modeling dogwhistles and mitigating their online harms.
