# Offensive Language Detection in Under-resourced Algerian Dialectal Arabic Language

arXiv: https://arxiv.org/abs/2203.10024 · HTML: https://mlsanity.com/paper/2203.10024/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English](https://mlsanity.com/paper/2104.01443/) (2021)
- [Arabic Offensive Language Detection Using Machine Learning and Ensemble Machine Learning Approaches](https://mlsanity.com/paper/2005.08946/) (2020)
- [WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets](https://mlsanity.com/paper/2009.05456/) (2020)

## Built on by (most cited first, 4 of 4)

- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [Hate speech detection in algerian dialect using deep learning](https://mlsanity.com/paper/2309.11611/) (2023)
- [FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks](https://mlsanity.com/paper/2607.11279/) (2026)

## Abstract (arXiv)

This paper addresses the problem of detecting the offensive and abusive content in Facebook comments, where we focus on the Algerian dialectal Arabic which is one of under-resourced languages. The latter has a variety of dialects mixed with different languages (i.e. Berber, French and English). In addition, we deal with texts written in both Arabic and Roman scripts (i.e. Arabizi). Due to the scarcity of works on the same language, we have built a new corpus regrouping more than 8.7k texts manually annotated as normal, abusive and offensive. We have conducted a series of experiments using the state-of-the-art classifiers of text categorisation, namely: BiLSTM, CNN, FastText, SVM and NB. The results showed acceptable performances, but the problem requires further investigation on linguistic features to increase the identification accuracy.