Binary Faux-Hate Detection

• Binary faux-hate detection is the process of distinguishing superficially hateful texts from genuinely harmful content by analyzing context and coded language.
• The survey integrates techniques ranging from lexicon-driven models to multi-head Transformer paradigms, achieving micro-F1 scores around 0.83 and reducing false positives by up to 43%.
• The research highlights challenges like evolving hate code lexicons and cross-lingual adaptation, underscoring the need for dynamic, context-aware detection frameworks.

Binary faux-hate detection refers to the automated identification of text that superficially appears to be hate speech but is, in fact, benign (“false positives”), as well as the detection of hate speech masked by non-explicit, coded, or contextually ambiguous language. This challenge is central to the deployment of robust moderation systems in large-scale social media environments. The task spans a spectrum from simple binary hate/non-hate detection to sophisticated multi-task settings that distinguish between true hate, offensive language, codeword usage, and narrative manipulation. This article surveys the principal methodologies, evaluation strategies, model architectures, and open challenges in binary faux-hate detection, integrating approaches that span lexicon-driven, feature-engineered, representation-learning, and multi-head Transformer paradigms.

1. Conceptual Foundations and Taxonomy

Historically, binary hate speech detection has conflated strongly negative sentiment, profanity, and group-targeted hostility under a single “hate” label, resulting in frequent misclassifications of emotionally charged but non-hateful language (“faux-hate”) (Naznin et al., 2024). In parallel, adversarial communities have developed encoded language (“hate codewords”) designed to evade traditional keyword-based classifiers, further complicating detection (Magu et al., 2017, Taylor et al., 2017). Binary faux-hate detection, as rigorously formulated, seeks to disambiguate between:

• Faux-positives: Benign utterances that trigger hate speech detectors due to context-independent overlap with flagged terms, strong emotive content, or ambiguous intent.
• Disguised hate: Posts using codewords, context-inverted semantics, or fabricated narratives that actualize group-targeted hate but evade standard lexicon-based filtering (Bhaskar et al., 18 Dec 2025).

The task is variably instantiated as:

• Binary hate vs. non-hate (conflating or separating offensive, emotional, and benign categories) (Themeli et al., 2021).
• Binary fake (fabricated narrative) vs. hate (genuine group-targeted incitement) in code-mixed settings (Bhaskar et al., 18 Dec 2025).
• Binary detection of coded hate vs. benign codeword use (Magu et al., 2017).
• Token/post-level flagging of contextual codeword usage (Taylor et al., 2017).

2. Datasets, Annotation, and Preprocessing

Datasets for binary faux-hate detection derive from multiple sources:

• Standard hate/offensive corpora: Davidson et al. (2017), Waseem & Hovy (2016), and Kaggle Toxic Comment datasets, often relabeled for binary tasks by merging ambiguous classes (Naznin et al., 2024, Themeli et al., 2021).
• Code-mixed social media datasets: Roman-script Hindi-English posts with labels FAKE (fabricated narrative) vs. HATE (Bhaskar et al., 18 Dec 2025).
• Community-driven codeword corpora: Tweets harvested from extremist networks (e.g., HateComm), with codeword annotation based on usage context rather than surface form, yielding high inter-annotator agreement (Krippendorff’s α = 0.871) (Taylor et al., 2017).
• Explicit codeword datasets: Tweets containing known substitutes (“Google(s)”, “Skype(s)”, etc.) annotated for genuine hate vs. benign use (Magu et al., 2017).

Preprocessing universally includes lowercasing, removal or normalization of URLs and mentions, tokenization (with BPE or standard word/character tokenizers depending on architecture), and class balancing. Codeword detection pipelines apply context-aware normalization (collapsing repeated characters, mapping emojis, etc.) and, in community-sourced settings, include account-level features (Bhaskar et al., 18 Dec 2025, Taylor et al., 2017).

3. Representational and Modeling Approaches

Table: Principal Representations and Detectors

Representation	Approach	Core Classifier(s)
BoW/TF-IDF	Lexicon-driven	LR, SVM, MLP
Pre-trained embeddings	GloVe/Word2Vec	NN (avg-pooled)
N-gram Graphs (NGG)	Contextual similarity	LR, NN
Contextual embeddings	Transformer-based	(Multi-head) Transformer
Community-contextual	Domain-specific embeddings + codeword graphs	LR

BoW and explicit hate-keyword frequency measures provide strong baseline performance but are susceptible to both false positives (non-hate profanity) and false negatives (masked hate) (Themeli et al., 2021). NGG features, wherein similarity to class-specific (hate/clean) n-gram graphs is computed, ameliorate some context-related errors. Combination features (BoW+GloVe+NGG) with LR/NN consistently yield micro-F₁ ≈ 0.83 (Themeli et al., 2021).

Transformer-based approaches implement shared-encoder architectures with parallel heads, either for multi-task hate-sentiment discrimination or joint fake/hate + target/severity prediction. Sentiment heads in multi-task frameworks provide a mechanism to reduce misclassification of emotionally negative but non-hateful content (Naznin et al., 2024). Codeword-sensitive models leverage skip-gram and dependency-based domain-specific embeddings trained on extremist communities, using graph expansion and PageRank to surface emergent code words and flag contextually likely faux-hate uses (Taylor et al., 2017). Binary classifiers (ridge-regularized logistic regression, SVM, or small MLPs) sit atop these representations.

4. Training Protocols and Loss Functions

Classical classifiers (LR/SVM/NN) optimize binary cross-entropy or hinge loss over hand-engineered features, with regularization and internal cross-validation for hyperparameter tuning (Themeli et al., 2021, Magu et al., 2017).

Transformer-based multi-task detectors combine cross-entropy losses on each head in a weighted sum,

$$L = \alpha L_\text{hate}(\hat y_\text{hate}, t_\text{hate}) + (1-\alpha) L_\text{sent}(\hat y_\text{sent}, t_\text{sent})$$

with typical α values in [0.6, 0.8]. Sentiment heads are first pre-trained (with α=0) and then both heads jointly fine-tuned (α=0.7) (Naznin et al., 2024). In code-mixed fake/hate classification, dual-head MLPs on top of RoBERTa use loss

$$L_\text{total} = \lambda_\text{binary} L_\text{binary} + \lambda_\text{multi} L_\text{multi}$$

with λ values chosen via grid search (best at λ=0.5) (Bhaskar et al., 18 Dec 2025). Residual connections around MLP heads can improve gradient flow and F1-minority class performance.

5. Evaluation, Error Analysis, and False-Positive Mitigation

Evaluation employs macro- and micro-averaged F₁, accuracy, precision, and recall, always reporting per-class performance to avoid class imbalance artifacts (Themeli et al., 2021, Bhaskar et al., 18 Dec 2025). Macro-F₁ is the principal metric for balanced binary tasks.

Key observed outcomes include:

• Best feature-engineered systems (BoW+GloVe+NGG + LR/NN): micro-F₁ = 0.831 (Themeli et al., 2021).
• Linear SVMs using codeword-laden BoW: precision ≈ 0.795, recall ≈ 0.794, F1 ≈ 0.80 (Magu et al., 2017).
• Contextual codeword detectors (domain-trained embeddings + graph expansion): F₁ ≈ 0.80, +0.15 over static keyword baseline (Taylor et al., 2017).
• Transformer-based hate + sentiment architecture: Precision (hate=1): 0.91, Recall: 0.97, F1: 0.94; false-positive rate drops from 28% → 16%, a 43% relative reduction via the sentiment head (Naznin et al., 2024).
• Dual-head RoBERTa on code-mixed fake/hate: Macro-F₁ = 0.765 with residual MLPs, +0.05 over single-task (Bhaskar et al., 18 Dec 2025).

Ablation and error studies demonstrate that multi-task or contextual features mitigate false positives on emotionally negative but non-hateful text. Faux-hate is further reduced by mixing context-aware NGG signals and sentiment polarity detection. However, representation choice and classifier choice both have highly significant effects (ANOVA p < 2e−16) (Themeli et al., 2021).

6. Contextualization, Adaptivity, and Open Challenges

Dynamic codeword lexicons require continuous expansion—communities invent new tokens to obfuscate hate (Magu et al., 2017, Taylor et al., 2017). Community-detection based sampling concentrates these signals, supporting more effective contextual embedding training. Annotator agreement is markedly higher (α=0.871) in community-grounded datasets versus simple keyword-driven samples, highlighting the importance of context (Taylor et al., 2017).

Key limitations identified include:

• Static lexicons and naïve BoW yield poor generalization to evolving coded language.
• Contextual awareness is necessary but insufficient for certain creative or oblique hate speech constructions.
• Transformer architectures, despite state-of-the-art results, require significant data and careful loss balancing; sentiment/target-aware heads demonstrably reduce faux-hate misclassifications but require curated auxiliary tasks (Naznin et al., 2024, Bhaskar et al., 18 Dec 2025).
• Cross-lingual, code-mixed, and multi-domain generalization remain difficult (Bhaskar et al., 18 Dec 2025).

A plausible implication is that progress in binary faux-hate detection will hinge on joint advances in domain-adaptive, context-sensitive representation learning, dynamic lexicon induction, and multi-task curriculum training frameworks that explicitly encode the various axes of hate, sentiment, emotion, and narrative veracity.