Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection
Hamid Reza Firoozfar, Mohammadsadegh Abolhasani, Reza Mousavi, Paul Jen-Hwa Hu
Read on arXiv →
Key claim
New taxonomy improves detection of coded language in moderation.
In plain English
Imagine you're trying to keep track of how people communicate sensitive topics on social media without getting flagged for moderation. Users often come up with clever ways to say things indirectly, using euphemisms or coded language to mask their true intent. This can make it really hard for content moderation systems to catch harmful content because the language is so nuanced and context-dependent. When these systems rely on existing frameworks, they often miss the mark, leading to either over-moderation or under-moderation — this is what's called a failure to detect algospeak or adversarial obfuscation effectively.
To address this, the authors propose a new way to categorize these indirect expressions based on the mechanisms behind how meaning is encoded and decoded, rather than just the communicative goals. They developed a comprehensive taxonomy that helps to systematically identify and analyze these expressions. By testing this taxonomy with 2,000 posts from platforms like TikTok and Bluesky, they found that it significantly outperformed previous taxonomies, achieving better accuracy and F1 scores.
In practical terms, this means that if you're building a content moderation tool, using this new taxonomy could help you better detect and understand the emerging coded language that users employ to evade moderation. It provides a more stable framework for identifying harmful content while respecting the nuances of user expression.
The paper introduces a new taxonomy for indirect linguistic expressions, which is a meaningful extension of existing frameworks.
The evaluation against multiple existing taxonomies and a solid dataset supports the claims made.
Deep reliability assessment
The methodology supports the narrower claim that, on their 2,000 English TikTok and Bluesky posts, a comprehensive mechanism-oriented taxonomy improves prompt-based LLM detection versus four prior taxonomies and a no-taxonomy prompt. It overreaches if read as proving robust moderation performance in production, because it does not test multimodal content, non-English or code-switched settings, adversarial adaptation after deployment, or stronger non-prompt baselines.
Reproducibility
Partially reproducible: code and the publicly releasable portion of the dataset are released on GitHub, but TikTok-derived text is not redistributed because of data-sharing restrictions.
Key figure
Figure 1 shows a TikTok example where the direct term porn star returns no results, while the same meaning is encoded through a sequence of emojis combining several mechanisms such as pictorial resemblance and name-based mapping.