Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

Hamid Reza Firoozfar; Mohammadsadegh Abolhasani; Paul Jen-Hwa Hu; Reza Mousavi

arxiv: 2606.27314 · v1 · pith:32ABA5X5new · submitted 2026-06-25 · 💻 cs.CL

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 This is my paper

Pith reviewed 2026-06-26 03:57 UTC · model grok-4.3

classification 💻 cs.CL

keywords indirect linguistic encodingcoded language detectionLLM promptingsocial media moderationtaxonomyalgospeakeuphemismsadversarial obfuscation

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The pith

A mechanism-oriented taxonomy of indirect linguistic encoding improves LLM detection of coded social media language over existing methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a taxonomy that organizes indirect linguistic expressions according to the mechanisms used to encode and recover meaning rather than the goals behind them. This taxonomy is tested by embedding it in prompts for three large language models and measuring performance on detecting such expressions in 2,000 annotated posts from TikTok and Bluesky. It outperforms four existing taxonomies and a baseline without taxonomy guidance. Sympathetic readers would see value in a more stable way to identify evolving forms of coded language that evade moderation. The results highlight how focusing on underlying operations can make detection more reliable across different models and platforms.

Core claim

The authors present a comprehensive taxonomy of indirect linguistic encoding centered on mechanisms such as substitution, abbreviation, and contextual inference. When this taxonomy guides LLM prompts, it achieves the best document-level and span-level detection results, with 4.7 percent higher accuracy and 5.4 percent higher F1 score than the strongest benchmark taxonomy on the annotated dataset.

What carries the argument

The mechanism-oriented taxonomy, which groups indirect expressions by the operations through which meaning is encoded and recovered.

If this is right

The taxonomy serves as a stable scaffold for detecting emerging coded language.
It supplies a useful input to content moderation pipelines.
Gains appear consistently across three LLMs at both document and span levels.
Abstracting away from communicative goals yields broader applicability than intent-based alternatives.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same mechanism categories could be applied to posts from additional platforms to test cross-site consistency.
Embedding the taxonomy in real-time moderation systems might lower missed detections of novel euphemisms.
Automated extraction of new mechanism instances from fresh data could keep the taxonomy current without full re-annotation.

Load-bearing premise

The 2,000 manually annotated posts from TikTok and Bluesky provide reliable ground truth labels for both document-level and span-level presence of indirect linguistic expressions.

What would settle it

A new collection of social media posts where an LLM prompted with the proposed taxonomy fails to exceed the accuracy or F1 of the best benchmark taxonomy would falsify the performance advantage.

Figures

Figures reproduced from arXiv: 2606.27314 by Hamid Reza Firoozfar, Mohammadsadegh Abolhasani, Paul Jen-Hwa Hu, Reza Mousavi.

**Figure 2.** Figure 2: Overview of the taxonomy development process. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the proposed taxonomy: the eleven top-level mechanism classes and their correspond [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper offers a mechanism-based taxonomy for coded language that edges out prior ones by a few points on 2000 annotated posts, but the gains rest entirely on unverified manual labels.

read the letter

The paper introduces a mechanism-oriented taxonomy for indirect linguistic encoding and shows it gives modest improvements in LLM detection of coded language on social media posts.

It does a clean job of separating the how of encoding from the why, which sets it apart from the four taxonomies it benchmarks against. Incorporating the taxonomy into prompts and running the comparison on 2000 annotated examples is a direct way to test the idea, and the 4.7% accuracy and 5.4% F1 gains are reported clearly.

The main limitation is that the entire result hinges on the quality of those manual annotations from TikTok and Bluesky. No information is given on inter-annotator agreement or the annotation guidelines, so any advantage could be an artifact of how the data was labeled rather than a property of the taxonomy. The gains are also small enough that they might not hold up under different prompting setups or larger datasets.

This work is for researchers in computational linguistics who focus on content moderation and adversarial language. A reader looking for a new way to structure prompts for detection tasks would find the taxonomy worth trying out.

I think it deserves peer review. The taxonomy is a fresh angle and the empirical setup is straightforward, so referees can sort out the annotation details and see if the improvements are reliable.

Referee Report

1 major / 1 minor

Summary. The paper proposes a mechanism-oriented taxonomy of indirect linguistic expressions (ILE) that categorizes encoding operations rather than communicative goals. It integrates the taxonomy into LLM prompts and evaluates it against four existing taxonomies and a no-taxonomy baseline on 2,000 manually annotated TikTok and Bluesky posts, reporting that the new taxonomy yields the best document- and span-level performance across three LLMs with gains of 4.7% accuracy and 5.4% F1 over the strongest benchmark.

Significance. If the ground-truth labels are reliable, the taxonomy could provide a stable, mechanism-focused scaffold for detecting emerging coded language and support content moderation. The work is strengthened by its direct empirical comparisons and focus on abstraction from surface forms. However, the absence of any reported validation for the annotations substantially limits the strength of the performance claims and their generalizability.

major comments (1)

[Evaluation] Evaluation section (dataset and annotation description): All reported performance gains (4.7% accuracy, 5.4% F1) rest exclusively on the 2,000 manually annotated posts as ground truth for both document- and span-level tasks. No inter-annotator agreement statistics, annotation protocol, number of annotators, span-boundary guidelines, or external validation are provided. This is load-bearing for the central claim; without these details, the observed advantage over the four benchmarks could be an artifact of labeling inconsistencies rather than a property of the taxonomy.

minor comments (1)

[Abstract] Abstract: the disclaimer regarding profane content is present but could usefully note the specific platforms and annotation scope for reader context.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the evaluation section. We address the major comment below and will revise the manuscript to incorporate the requested details.

read point-by-point responses

Referee: [Evaluation] Evaluation section (dataset and annotation description): All reported performance gains (4.7% accuracy, 5.4% F1) rest exclusively on the 2,000 manually annotated posts as ground truth for both document- and span-level tasks. No inter-annotator agreement statistics, annotation protocol, number of annotators, span-boundary guidelines, or external validation are provided. This is load-bearing for the central claim; without these details, the observed advantage over the four benchmarks could be an artifact of labeling inconsistencies rather than a property of the taxonomy.

Authors: We agree that the absence of detailed annotation information limits the interpretability of the reported gains and that these details are essential to substantiate the ground-truth reliability. The current manuscript provides only a high-level mention of the 2,000 manually annotated posts without describing the protocol, annotator count, IAA metrics, span guidelines, or validation steps. In the revised version we will add a dedicated subsection to the Evaluation section that fully documents the annotation process, including these elements, to allow readers to assess whether the taxonomy-driven improvements are robust to labeling variation. revision: yes

Circularity Check

0 steps flagged

No circularity: evaluation rests on external annotations and independent benchmarks

full rationale

The paper introduces a mechanism-oriented taxonomy and evaluates it by embedding the taxonomy into LLM prompts, then measuring document- and span-level accuracy/F1 on 2,000 manually annotated TikTok/Bluesky posts against four existing taxonomies plus a no-taxonomy baseline. No equations, fitted parameters, or self-citation chains are described that would reduce the reported 4.7% accuracy / 5.4% F1 gains to the paper's own inputs by construction. The central empirical claim is therefore measured against externally supplied ground truth and independent comparator taxonomies, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the proposed taxonomy capturing relevant encoding mechanisms and on the quality of the human annotations used for evaluation.

axioms (1)

domain assumption The manually annotated dataset accurately represents instances of indirect linguistic encoding at document and span levels.
Performance metrics are computed against these annotations as ground truth.

pith-pipeline@v0.9.1-grok · 5739 in / 1119 out tokens · 76557 ms · 2026-06-26T03:57:51.914933+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references

[1]

Robert C Nickerson, Upkar Varshney, and Jan Muntermann

The emerging science of content labeling: Contextualizing social media content moderation.Journal of the Association for Information Science and Technology, 73(10):1365–1386. Robert C Nickerson, Upkar Varshney, and Jan Muntermann. 2013. A method for taxonomy development and its application in information systems.European Journal of Information Systems, 22...

arXiv 2013
[2]

Orthographic Transformation Visual Similarity Substi- tution A target expression is altered by replacing one or more letters with visually similar characters, numbers, or symbols, while preserving the word’s ordinary reading orientation. w33d→weed Inversion Based Glyph Encoding A target expression is encoded using numbers or glyphs whose meaning becomes r...
[3]

seggs→sex not see→Nazi Alphanumeric Rebus Letters or numbers are used for their phonetic value to rep- resent sounds, syllables, or words in the target expression

Phonetic Substitution Near Homophone Drift A lexical substitute approximates the sound of a target ex- pression while altering its spelling or lexical form. seggs→sex not see→Nazi Alphanumeric Rebus Letters or numbers are used for their phonetic value to rep- resent sounds, syllables, or words in the target expression. CU46 → see you for sex Phonological ...
[4]

Formal Compression Initialism Acronym A multiword target expression is compressed by retaining the initial letters, or a pronounceable subset, of its compo- nent words. SH→self-harm DV → Domestic Vio- lence Lexical Clipping A single lexical item is shortened by removing one or more segments while preserving a recognizable fragment that stands for the full...
[5]

Formal Encoding Systems Classical Cipher A target expression is transformed using an established cipher or cryptographic rule, producing an encoded form that requires applying the corresponding inverse procedure to decode. fhvpvqr → suicide (ROT13) Textual Algorithmic En- coding A target expression is encoded through an explicit rule ap- plied to letters,...
[6]

Conventional Sign Reassignment Lexical Reassignment An ordinary lexical item acquires a stable coded meaning by convention, without relying on visual, phonetic, or algo- rithmic transformation. accountant → sex worker Conventional Numeric Symbol Reassignment A number or symbol is assigned a stable coded meaning through social convention rather than a dete...
[7]

Morpho-lexical Encoding Blending A coded expression is formed by merging parts of two or more lexical items into a single new form, so that the source words remain partially recoverable. covidiot → COVID + id- iot Affix Manipulation A target expression is modified by adding productive pre- fixes, suffixes, or bound morphemes, creating a derived form whose...
[8]

Referential Alias Encoding Attribute Based Alias An indirect label that refers to a specific entity through a salient attribute, such as appearance, color, role, function, ownership, reputation, or platform design. the bald billionaire → Jeff Bezos orange-and-black YouTube→Pornhub Pseudonymous Labeling A substitute name, nickname, or moniker assigned to a...
[9]

Semantic Circumlocution Euphemism A socially softer, less explicit, or less stigmatized expression replaces a direct taboo or sensitive term. passed away→died spicy content → pornog- raphy Periphrasis A target concept is expressed through a longer descriptive phrase that identifies it by its function, effect, or associated activity rather than naming it d...
[10]

Metaphorical and Metonymic Encoding Continued on next page 18 Sub-mechanism Definition Examples Metaphorical Mapping A target concept is encoded through a source-domain ex- pression whose perceived qualities, appearance, function, or symbolic associations resemble the target. snow→cocaine taco→female genitalia Metonymic Encoding A target concept is encode...
[11]

Pictorial and Symbolic Encoding Referential Iconicity A pictorial symbol or emoji encodes a target meaning be- cause its visual appearance directly resembles, depicts, or conventionally symbolizes the referent. →cannabis →butt Eventive Iconicity A pictorial symbol or symbol combination encodes a target action, event, or practice by depicting an object, bo...
[12]

USA", "ASAP

Cross-linguistic Transformation Translation Based En- coding A target expression is replaced with its equivalent or near- equivalent in another language, allowing the sensitive mean- ing to be concealed from audiences or systems that primarily process the original language. muerte→death; chudai→sex/fucking Script Transliteration En- coding A target expres...

arXiv 2024

[1] [1]

Robert C Nickerson, Upkar Varshney, and Jan Muntermann

The emerging science of content labeling: Contextualizing social media content moderation.Journal of the Association for Information Science and Technology, 73(10):1365–1386. Robert C Nickerson, Upkar Varshney, and Jan Muntermann. 2013. A method for taxonomy development and its application in information systems.European Journal of Information Systems, 22...

arXiv 2013

[2] [2]

Orthographic Transformation Visual Similarity Substi- tution A target expression is altered by replacing one or more letters with visually similar characters, numbers, or symbols, while preserving the word’s ordinary reading orientation. w33d→weed Inversion Based Glyph Encoding A target expression is encoded using numbers or glyphs whose meaning becomes r...

[3] [3]

seggs→sex not see→Nazi Alphanumeric Rebus Letters or numbers are used for their phonetic value to rep- resent sounds, syllables, or words in the target expression

Phonetic Substitution Near Homophone Drift A lexical substitute approximates the sound of a target ex- pression while altering its spelling or lexical form. seggs→sex not see→Nazi Alphanumeric Rebus Letters or numbers are used for their phonetic value to rep- resent sounds, syllables, or words in the target expression. CU46 → see you for sex Phonological ...

[4] [4]

Formal Compression Initialism Acronym A multiword target expression is compressed by retaining the initial letters, or a pronounceable subset, of its compo- nent words. SH→self-harm DV → Domestic Vio- lence Lexical Clipping A single lexical item is shortened by removing one or more segments while preserving a recognizable fragment that stands for the full...

[5] [5]

Formal Encoding Systems Classical Cipher A target expression is transformed using an established cipher or cryptographic rule, producing an encoded form that requires applying the corresponding inverse procedure to decode. fhvpvqr → suicide (ROT13) Textual Algorithmic En- coding A target expression is encoded through an explicit rule ap- plied to letters,...

[6] [6]

Conventional Sign Reassignment Lexical Reassignment An ordinary lexical item acquires a stable coded meaning by convention, without relying on visual, phonetic, or algo- rithmic transformation. accountant → sex worker Conventional Numeric Symbol Reassignment A number or symbol is assigned a stable coded meaning through social convention rather than a dete...

[7] [7]

Morpho-lexical Encoding Blending A coded expression is formed by merging parts of two or more lexical items into a single new form, so that the source words remain partially recoverable. covidiot → COVID + id- iot Affix Manipulation A target expression is modified by adding productive pre- fixes, suffixes, or bound morphemes, creating a derived form whose...

[8] [8]

Referential Alias Encoding Attribute Based Alias An indirect label that refers to a specific entity through a salient attribute, such as appearance, color, role, function, ownership, reputation, or platform design. the bald billionaire → Jeff Bezos orange-and-black YouTube→Pornhub Pseudonymous Labeling A substitute name, nickname, or moniker assigned to a...

[9] [9]

Semantic Circumlocution Euphemism A socially softer, less explicit, or less stigmatized expression replaces a direct taboo or sensitive term. passed away→died spicy content → pornog- raphy Periphrasis A target concept is expressed through a longer descriptive phrase that identifies it by its function, effect, or associated activity rather than naming it d...

[10] [10]

Metaphorical and Metonymic Encoding Continued on next page 18 Sub-mechanism Definition Examples Metaphorical Mapping A target concept is encoded through a source-domain ex- pression whose perceived qualities, appearance, function, or symbolic associations resemble the target. snow→cocaine taco→female genitalia Metonymic Encoding A target concept is encode...

[11] [11]

Pictorial and Symbolic Encoding Referential Iconicity A pictorial symbol or emoji encodes a target meaning be- cause its visual appearance directly resembles, depicts, or conventionally symbolizes the referent. →cannabis →butt Eventive Iconicity A pictorial symbol or symbol combination encodes a target action, event, or practice by depicting an object, bo...

[12] [12]

USA", "ASAP

Cross-linguistic Transformation Translation Based En- coding A target expression is replaced with its equivalent or near- equivalent in another language, allowing the sensitive mean- ing to be concealed from audiences or systems that primarily process the original language. muerte→death; chudai→sex/fucking Script Transliteration En- coding A target expres...

arXiv 2024