youssefreda9/HAYAA

Hayā (حياء) — Arabic Toxic Content Dataset 🛡️

Dataset Description

Hayā is a large-scale, curated Arabic binary toxicity dataset built for training and evaluating Arabic content-moderation models. It was assembled by reviewing, collecting, standardizing, and merging 51 public Arabic hate-speech and abuse datasets into a single clean corpus of nearly 1 million examples.

This is the dataset used to train the Hayā model, a fine-tuned UBC-NLP/MARBERTv2 classifier that achieved 97.84% accuracy on the held-out test set.

Key Features

• ~997K examples of Arabic text (comments, posts, tweets) labeled as Safe (0) or Toxic (1)
• Every major Arabic dialect: Egyptian, Levantine, Gulf, Maghrebi, Iraqi, Sudanese, and MSA
• Multi-platform: Twitter, Facebook, YouTube, news comments, and more
• Deduplicated: Zero duplicate texts across the entire corpus
• Conflict-resolved: Overlapping texts from different sources resolved via majority vote
• Leak-free splits: Stratified 80/10/10 split with verified zero overlap between train, validation, and test

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Dataset Summary

Property	Value
Language	Arabic (all major dialects)
Task	Binary text classification
Labels	0 = Safe, 1 = Toxic
Total Size	997,589 examples
Train	798,071 (80%)
Validation	99,759 (10%)
Test	99,759 (10%)
Sources	51 public datasets merged & standardized
Format	Parquet

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What "Toxic" Covers

The Toxic label encompasses:

• 🤬 Profanity & offensive language
• 🔥 Hate speech
• 😡 Insults, harassment & cyberbullying
• 🚫 Racism & religious hate
• ⚠️ Sexism & sexually explicit language
• 🔤 Morphologically complex and context-dependent expressions
• 🕵️ Intentional typos / obfuscation attempts

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Dialects Covered

Dialect Group	Varieties
Egyptian	Egyptian Arabic
Levantine	Syrian, Lebanese, Jordanian, Palestinian
Gulf	Saudi, Emirati, Kuwaiti, Bahraini, Omani, Qatari
Maghrebi	Moroccan, Algerian, Tunisian, Libyan
Other	Iraqi, Sudanese, Yemeni
MSA	Modern Standard Arabic

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Data Fields

Field	Type	Description
text	string	The Arabic text (comment, tweet, or post)
label	int	0 = Safe, 1 = Toxic
source	string	Origin dataset(s), pipe-separated if from multiple sources
dialect	string	Dialect(s), pipe-separated if from multiple sources

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Data Splits

Split	Examples	Purpose
train	~798K	Model training
validation	~100K	Hyperparameter tuning & early stopping
test	~100K	Final held-out evaluation

All splits are stratified by label and verified for zero text overlap (no data leakage).

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Usage

from datasets import load_dataset

dataset = load_dataset("youssefreda9/HAYAA")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Example
print(train[0])
# {'text': '...', 'label': 0, 'source': 'OSACT4', 'dialect': 'MSA'}

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Curation Process

1. Collection

Reviewed and collected 100+ public Arabic hate-speech and abuse datasets from published research, shared tasks, and public repositories.

2. Standardization

Every source used a different labeling scheme (3-class, 5-class, multi-label, etc.). All were standardized into a single binary scheme:

• Safe (0): Non-toxic, normal text
• Toxic (1): Offensive, hateful, abusive, or harmful

3. Cleaning & Quality Control

• Removed problematic datasets: Sources with noisy labels, wrong task definitions, or exact duplicates of other sources were excluded
• Fixed known mislabels: Manual keyword-based correction of known systematic errors (e.g., SaudiCodeMixing safe-labeled profanity)
• Filtered noise: Removed long, machine-translated safe texts that didn't match real Arabic usage patterns

4. Conflict Resolution

Texts appearing in multiple sources with conflicting labels were resolved via majority vote.

5. Deduplication

Full text-level deduplication with dialect and source metadata merged (pipe-separated) for multi-source texts.

6. Splitting

Stratified 80/10/10 split (train/val/test) with seed=42, verified for zero leakage across all split pairs.

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Source Datasets

This corpus merges 51 standardized public Arabic datasets, including:

Dataset	Dialect	Size	Platform
L-HSAB	Levantine	~5.8K	Twitter
T-HSAB	Tunisian	~6K	Facebook/YouTube
OSACT4	MSA + mixed	~10K	Twitter
OSACT5	MSA + mixed	~12.7K	Twitter
MPOLD	Multi-dialect	~4K	Multi-platform
Let-Mi	Levantine	~6.6K	Twitter
ArMI	MSA, Egyptian, Gulf, Levantine	~9.8K	Twitter
ADHAR	Multi-dialect	~4.3K	Twitter
LREC	MSA + dialects	~15.9K	Twitter
... and 42 more

See the full Dataset Registry for detailed per-source documentation.

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Model Trained on This Dataset

The Hayā model — fine-tuned UBC-NLP/MARBERTv2 — achieves:

Metric	Score
Accuracy	97.84%
F1 (Toxic class)	94.12%
F1 (Safe class)	98.45%

Note: Manual error analysis showed the model frequently outperformed the original human annotations — many counted "errors" were actually mislabels in the source data.

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Intended Use

• ✅ Training and evaluating Arabic toxicity / hate-speech classifiers
• ✅ Benchmarking Arabic NLP models on content moderation
• ✅ Research on multi-dialect Arabic text classification
• ✅ Building content-moderation pipelines for Arabic platforms

Out-of-Scope Use

• ❌ Generating toxic or hateful content
• ❌ Surveillance or profiling of individuals
• ❌ Deployed decisions without human review

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Limitations & Biases

• Platform bias: The majority of the data comes from Twitter; other platforms (forums, messaging apps) are underrepresented.
• Annotation noise: Despite extensive cleaning, some label noise from original sources may remain.
• Dialect imbalance: Some dialects (e.g., Egyptian, MSA) are better represented than others (e.g., Sudanese, Yemeni).
• Temporal bias: Most data reflects Arabic social media discourse from 2018–2024; slang and evasion tactics evolve constantly.

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Citation

If you use this dataset, please cite:

@misc{hayaa2026,
  title={Hayā: A Large-Scale Multi-Dialect Arabic Toxicity Dataset},
  author={Youssef Reda},
  year={2026},
  url={https://huggingface.co/datasets/youssefreda9/HAYAA},
}

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License

This dataset is released under the Apache 2.0 License.

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Contact

• GitHub: youssefreda10/HAYAA
• Model: youssefreda9/HAYAA