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Hate speech detection in the Arabic language: corpus design, construction, and evaluation.

Ashraf Ahmad¹, Mohammad Azzeh², Eman Alnagi¹

¹Department of Computer Science, Princess Sumaya University for Technology (PSUT), Amman, Jordan.

Frontiers in Artificial Intelligence

|March 6, 2024

Summary

Researchers created a large Arabic hate speech dataset for better online detection. This dataset aids machine learning models in identifying harmful content across diverse dialects, improving accuracy.

Keywords:

Arabic hate speech Arabic hate speech corpus Arabic hate speech detection machine learning natural language processing (NLP)

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Area of Science:

Natural Language Processing
Machine Learning
Computational Linguistics

Background:

Arabic hate speech detection is complex due to linguistic diversity.
Existing methods lack comprehensive Arabic hate speech datasets.
Online platforms require effective tools to combat harmful content.

Purpose of the Study:

To introduce a novel, large-scale, multi-class Arabic hate speech dataset.
To evaluate machine learning model performance for Arabic hate speech identification.
To facilitate further research in Arabic online content moderation.

Main Methods:

Developed a public dataset of 403,688 annotated Arabic tweets.
Utilized text representation models: Word2Vec, TF-IDF, and AraBert.
Evaluated seven machine learning classifiers: SVM, LR, NB, RF, AdaBoost, XGBoost, and CatBoost.

Main Results:

The novel dataset proved efficient for hate speech detection tasks.
Encouraging assessment outcomes were achieved in challenging, unstructured text.
The dataset supports robust evaluation of various machine learning models.

Conclusions:

The created dataset is a valuable resource for Arabic hate speech research.
Machine learning models demonstrate potential for effective Arabic hate speech identification.
Further academic investigation into this critical area is now enabled.