Simple LLM based Approach to Counter Algospeak

Jan Fillies, Adrian Paschke

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.
Anthology ID:
2024.woah-1.10
Volume:
Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yi-Ling Chung, Zeerak Talat, Debora Nozza, Flor Miriam Plaza-del-Arco, Paul Röttger, Aida Mostafazadeh Davani, Agostina Calabrese
Venues:
WOAH | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–145
Language:
URL:
https://aclanthology.org/2024.woah-1.10
DOI:
Bibkey:
Cite (ACL):
Jan Fillies and Adrian Paschke. 2024. Simple LLM based Approach to Counter Algospeak. In Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024), pages 136–145, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Simple LLM based Approach to Counter Algospeak (Fillies & Paschke, WOAH-WS 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.woah-1.10.pdf
PDF Search