@inproceedings{de-kock-etal-2026-iykyk,
    title = "{IYKYK}: Using language models to decode extremist cryptolects",
    author = "de Kock, Christine  and
      Riabi, Arij  and
      Talat, Zeerak  and
      Schlichtkrull, Michael Sejr  and
      Madhyastha, Pranava  and
      Hovy, Eduard",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.393/",
    pages = "8393--8409",
    ISBN = "979-8-89176-380-7",
    abstract = "Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to detect and interpret the cryptolects of two online extremist platforms. Evaluating eight models across six tasks, our results indicate that general purpose LLMs cannot consistently detect or decode extremist language. However, performance can be significantly improved by domain adaptation and specialised prompting techniques. These results provide important insights to inform the development and deployment of automated moderation technologies. We further develop and release novel labelled and unlabelled datasets, including 19.4M posts from extremist platforms and lexicons validated by human experts."
}