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Datasets:
youssefreda9
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HAYAA

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
hate-speech-detection
Languages:
Arabic
Size:
100K - 1M
Tags:
arabic
toxicity
hate-speech
cyberbullying
content-moderation
offensive-language
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
language:
  - ar
license: apache-2.0
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
  - hate-speech-detection
tags:
  - arabic
  - toxicity
  - hate-speech
  - cyberbullying
  - content-moderation
  - offensive-language
  - profanity
  - multi-dialect
  - arabic-nlp
pretty_name: Hayā — Arabic Toxic Content Dataset
size_categories:
  - 100K<n<1M
source_datasets:
  - original
  - L-HSAB
  - T-HSAB
  - OSACT4
  - OSACT5
  - MPOLD
  - Let-Mi
  - ArMIS
  - ArMI
  - ADHAR
  - LREC
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype: int64
    - name: source
      dtype: string
    - name: dialect
      dtype: string
  splits:
    - name: train
      num_examples: 798071
    - name: validation
      num_examples: 99759
    - name: test
      num_examples: 99759
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: validation
        path: val.parquet
      - split: test
        path: test.parquet

Hayā (حياء) — Arabic Toxic Content Dataset 🛡️

Model on HF GitHub

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

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

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

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

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

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).

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'}

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.

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.

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.

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

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.

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},
}

License

This dataset is released under the Apache 2.0 License.

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