Beyond Surface Form: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Language Encoding for LLM-Based Coded Language Detection
Score
53Why selected
The paper presents an innovative and empirically validated ILE taxonomy that achieves significant performance gains in LLM-based detection, offering substantial practical value and methodological contributions to content safety.
AI commentary
「This taxonomy provides a systematic cognitive framework for LLMs to detect evolving online coded language and evasive language, potentially improving the accuracy and adaptability of content moderation on social platforms.」
Quick overview
Social media users often employ indirect linguistic expressions (ILE) to evade content moderation, such as algospeak, euphemisms, and adversarial obfuscation. Existing taxonomies focus on communicative intent, leading to unstable detection. This paper proposes a comprehensive, mechanism-oriented taxonomy that abstracts underlying encoding operations and integrates it into LLM prompts. On 2,000 manually annotated TikTok and Bluesky posts, the new taxonomy achieves the strongest document- and span-level performance across three LLMs, with a 4.7% accuracy improvement and 5.4% F1 improvement, providing a stable scaffold for detecting emerging coded language.
- Proposes a comprehensive, mechanism-oriented taxonomy of indirect linguistic expressions (ILE) focusing on underlying encoding operations rather than communicative intent.
- Integrates the taxonomy into LLM prompts and evaluates on 2,000 manually annotated social media posts.
- Achieves 4.7% accuracy and 5.4% F1 improvement over the best-performing benchmark compared to four existing taxonomies and a no-taxonomy baseline.
- Highlights the importance of a comprehensive, mechanism-oriented taxonomy as a stable scaffold for detecting emerging coded language.
Full text
Computer Science > Computation and Language
arXiv:2606.27314v1 (cs) [Submitted on 25 Jun 2026]Title:Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection
Authors:Hamid Reza Firoozfar, Mohammadsadegh Abolhasani, Reza Mousavi, Paul Jen-Hwa Hu View a PDF of the paper titled Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection, by Hamid Reza Firoozfar and 3 other authors View PDFAbstract:To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanings. Such expressions surface as algospeak, euphemisms, and adversarial obfuscation, depending on intent and context, and they involve recurring encoding mechanisms. We propose a comprehensive, mechanism-oriented taxonomy of ILE that abstracts away from communicative goals and instead categorizes the underlying operations through which meaning is encoded and recovered. We evaluate the taxonomy by incorporating it into LLM prompts and comparing it with four existing taxonomies and a no-taxonomy baseline, using 2,000 manually annotated TikTok and Bluesky posts. The proposed taxonomy attains the strongest document- and span-level performance across the three LLMs, achieving an improvement of 4.7% in accuracy and 5.4% in F1 over the best-performing benchmark. The empirical results reveal the importance of a comprehensive, mechanism-oriented taxonomy as a stable scaffold for detecting emerging coded language and a useful input to content moderation. Disclaimer: This paper contains content that may be profane, vulgar, or offensive.
| Comments: | Submitted for review in ARR for EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2606.27314 [cs.CL] |
| (or arXiv:2606.27314v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27314 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Mohammadsadegh Abolhasani [view email][v1] Thu, 25 Jun 2026 17:29:45 UTC (2,055 KB)
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