Toxic language detection: a systematic review of Arabic datasets

TL;DR

This paper systematically reviews 54 Arabic datasets for toxic language detection, analyzing their features and gaps to guide future research in this emerging field.

Contribution

It provides a comprehensive analysis of existing Arabic toxic language datasets, highlighting gaps and offering recommendations for future dataset development.

Findings

Identified key gaps in dataset availability and content

Provided a detailed analysis framework for dataset evaluation

Shared a curated list of datasets in a public repository

Abstract

The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need. This paper offers a comprehensive survey of Arabic datasets focused on online toxic language. We systematically gathered a total of 54 available datasets and their corresponding papers and conducted a thorough analysis, considering 18 criteria across four primary dimensions: availability details, content, annotation process, and reusability. This analysis enabled us to identify existing gaps and make recommendations for future research works. For the convenience of the research community, the list of the analysed datasets is maintained in a GitHub repository (https://github.com/Imene1/Arabic-toxic-language).