@inproceedings{fillies-paschke-2024-simple,
    title = "Simple {LLM} based Approach to Counter Algospeak",
    author = "Fillies, Jan  and
      Paschke, Adrian",
    editor = {Chung, Yi-Ling  and
      Talat, Zeerak  and
      Nozza, Debora  and
      Plaza-del-Arco, Flor Miriam  and
      R{\"o}ttger, Paul  and
      Mostafazadeh Davani, Aida  and
      Calabrese, Agostina},
    booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.woah-1.10/",
    doi = "10.18653/v1/2024.woah-1.10",
    pages = "136--145",
    abstract = "With the use of algorithmic moderation on online communication platforms, an increase in adaptive language aiming to evade the automatic detection of problematic content has been observed. One form of this adapted language is known as ``Algospeak'' and is most commonly associated with large social media platforms, e.g., TikTok. It builds upon Leetspeak or online slang with its explicit intention to avoid machine readability. The machine-learning algorithms employed to automate the process of content moderation mostly rely on human-annotated datasets and supervised learning, often not adjusted for a wide variety of languages and changes in language. This work uses linguistic examples identified in research literature to introduce a taxonomy for Algospeak and shows that with the use of an LLM (GPT-4), 79.4{\%} of the established terms can be corrected to their true form, or if needed, their underlying associated concepts. With an example sentence, 98.5{\%} of terms are correctly identified. This research demonstrates that LLMs are the future in solving the current problem of moderation avoidance by Algospeak."
}