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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.05635 |
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| _version_ | 1866909640878456832 |
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| author | de Kock, Christine Riabi, Arij Talat, Zeerak Schlichtkrull, Michael Sejr Madhyastha, Pranava Hovy, Ed |
| author_facet | de Kock, Christine Riabi, Arij Talat, Zeerak Schlichtkrull, Michael Sejr Madhyastha, Pranava Hovy, Ed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IYKYK: Using language models to decode extremist cryptolects de Kock, Christine Riabi, Arij Talat, Zeerak Schlichtkrull, Michael Sejr Madhyastha, Pranava Hovy, Ed Computation and Language 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. |
| title | IYKYK: Using language models to decode extremist cryptolects |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.05635 |