  Online hate speech proliferation has created a difficult problem for social
media platforms. A particular challenge relates to the use of coded language by
groups interested in both creating a sense of belonging for its users and
evading detection. Coded language evolves quickly and its use varies over time.
This paper proposes a methodology for detecting emerging coded hate-laden
terminology. The methodology is tested in the context of online antisemitic
discourse. The approach considers posts scraped from social media platforms,
often used by extremist users. The posts are scraped using seed expressions
related to previously known discourse of hatred towards Jews. The method begins
by identifying the expressions most representative of each post and calculating
their frequency in the whole corpus. It filters out grammatically incoherent
expressions as well as previously encountered ones so as to focus on emergent
well-formed terminology. This is followed by an assessment of semantic
similarity to known antisemitic terminology using a fine-tuned large language
model, and subsequent filtering out of the expressions that are too distant
from known expressions of hatred. Emergent antisemitic expressions containing
terms clearly relating to Jewish topics are then removed to return only coded
expressions of hatred.
