DOI: 10.5281/zenodo.17998261
title: MAGHREB-HOF: A Large-Scale Annotated Corpus for Hate and Offensive Language Detection in Maghrebi Arabic

description: MAGHREB-HOF is a large-scale annotated corpus for hate and offensive language detection in Maghrebi Arabic, including Arabizi and code-mixed Arabic–French/English social media text. The dataset is designed to support reproducible benchmarking on noisy North-African online language, where spelling variation, dialect mixing, and transliteration are common.

What this record contains

This Zenodo record provides:





The annotated corpus of Maghrebi social media comments (anonymized).


The Guidelines on pdf format



A high-confidence, class-balanced subset used for the main binary classification experiments reported in the companion paper.




Annotation label definitions and metadata to enable reproducibility.



Source and collection

Comments were collected from public social-media pages/posts relevant to the Maghrebi region. The released data are anonymized and do not include direct personal identifiers.

Annotation and labels

Each comment is annotated for hate/offensive content.





Binary label: 0 = non-offensive/clean, 1 = hate/offensive .




If included, additional columns may capture auxiliary information (e.g., multi-class categories or confidence / agreement indicators). See LABELS.md and the guidline for definitions.



Core columns (typical):





id — unique comment identifier (internal)




Comments — anonymized comment text




label — binary label (0/1)-for multi -classification(0/6)




(optional) IDANNOTATORS, PAGE, POST, POSTEDATE, etc.



Recommended use

The dataset can be used to train and evaluate classifiers for:





hate/offensive language detection,




toxicity detection,




dialect-robust NLP for Maghrebi Arabic and Arabizi,under standard experimental protocols (cross-validation or fixed hold-out splits as described in the paper).



Ethical considerations

This dataset contains potentially harmful language. It is released for research purposes to support detection and mitigation of harmful content. The data are anonymized, and direct identifiers are removed. Users of the dataset should follow applicable ethical guidelines and local regulations when working with social-media data.

License





Dataset license: Creative Commons Attribution 4.0 International (CC BY 4.0)Users must provide appropriate credit and cite the companion paper / this Zenodo record.



Access / Embargo

This record is published with an embargo. Files will become available automatically on 2026-12-31 (or “on the date of publication of the companion article”). Metadata and DOI are visible immediately.

Related publication

Companion paper: *مقارنة نماذج الكلمات والمحارف لكشف خطاب الكراهية في العربية المغاربية*  submitted to  Indonesian Journal of Electrical Engineering and Computer Science, [2025].

Contact

For questions or embargo-related access requests during peer review:





Corresponding author: Ahmed Zoubir Messalti — ahmedzoubir.messalti@univ-setif.dz



Keywords: Maghrebi Arabic; Arabizi; code-mixing; hate speech; offensive language; toxicity detection; NLP; social media.

Dataset statistics dashboard

An interactive dashboard with descriptive statistics (label distribution, metadata summaries, and basic exploratory views) is available at:http://magh-arabic-hof.duckdns.org:3000/public/dashboard/57c49829-0c27-4018-899e-e0eabf6c8c53?tab=227-page-1



Note: The dashboard is provided for exploration and reporting. It does not grant access to embargoed files and may be updated over time.


Annotation demo (Doccano) for peer review

To support peer review and demonstrate the annotation workflow, a Doccano instance is available for reviewers to explore the labeling interface on a small demo project.





Doccano login page:http://magh-arabic-hof.duckdns.org:8800/auth




Reviewer username:IJEECS




Password: provided privately to reviewers upon request (not published publicly).