  It is a well-known approach for fringe groups and organizations to use
euphemisms -- ordinary-sounding and innocent-looking words with a secret
meaning -- to conceal what they are discussing. For instance, drug dealers
often use "pot" for marijuana and "avocado" for heroin. From a social media
content moderation perspective, though recent advances in NLP have enabled the
automatic detection of such single-word euphemisms, no existing work is capable
of automatically detecting multi-word euphemisms, such as "blue dream"
(marijuana) and "black tar" (heroin). Our paper tackles the problem of
euphemistic phrase detection without human effort for the first time, as far as
we are aware. We first perform phrase mining on a raw text corpus (e.g., social
media posts) to extract quality phrases. Then, we utilize word embedding
similarities to select a set of euphemistic phrase candidates. Finally, we rank
those candidates by a masked language model -- SpanBERT. Compared to strong
baselines, we report 20-50% higher detection accuracies using our algorithm for
detecting euphemistic phrases.
