# Arabic Offensive Language on Twitter: Analysis and Experiments

arXiv: https://arxiv.org/abs/2004.02192 · HTML: https://mlsanity.com/paper/2004.02192/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 29 of 29)

- [Pre-Training BERT on Arabic Tweets: Practical Considerations](https://mlsanity.com/paper/2102.10684/) (2021)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features](https://mlsanity.com/paper/2310.06458/) (2023)
- [Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech](https://mlsanity.com/paper/2201.06723/) (2022)
- [KOLD: Korean Offensive Language Dataset](https://mlsanity.com/paper/2205.11315/) (2022)
- [ViHOS: Hate Speech Spans Detection for Vietnamese](https://mlsanity.com/paper/2301.10186/) (2023)
- [Offensive Hebrew Corpus and Detection using BERT](https://mlsanity.com/paper/2309.02724/) (2023)
- [Cross-lingual Emotion Detection](https://mlsanity.com/paper/2106.06017/) (2021)
- [Predicting the Type and Target of Offensive Social Media Posts in Marathi](https://mlsanity.com/paper/2211.12570/) (2022)
- [Detecting and Reasoning of Deleted Tweets before they are Posted](https://mlsanity.com/paper/2305.04927/) (2023)
- [A Federated Learning Approach to Privacy Preserving Offensive Language Identification](https://mlsanity.com/paper/2404.11470/) (2024)
- [WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments](https://mlsanity.com/paper/2108.00057/) (2021)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [Zero-shot Visual Commonsense Immorality Prediction](https://mlsanity.com/paper/2211.05521/) (2022)
- [SOLD: Sinhala Offensive Language Dataset](https://mlsanity.com/paper/2212.00851/) (2022)
- [Hate speech detection in algerian dialect using deep learning](https://mlsanity.com/paper/2309.11611/) (2023)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [Culture Matters in Toxic Language Detection in Persian](https://mlsanity.com/paper/2506.03458/) (2025)
- [K/DA: Automated Data Generation Pipeline for Detoxifying Implicitly Offensive Language in Korean](https://mlsanity.com/paper/2506.13513/) (2025)
- [Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis](https://mlsanity.com/paper/2403.01921/) (2024)
- [Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets](https://mlsanity.com/paper/2407.02448/) (2024)
- [Exploratory Arabic Offensive Language Dataset Analysis](https://mlsanity.com/paper/2101.11434/) (2021)
- [Automatic Expansion and Retargeting of Arabic Offensive Language Training](https://mlsanity.com/paper/2111.09574/) (2021)
- [The Rise of AfricaNLP: A Survey of Contributions, Contributors, Community Impact, and Bibliometric Analysis](https://mlsanity.com/paper/2509.25477/) (2025)
- [ArabDiscrim: A Decade-Long Arabic Facebook Corpus on Racism and Discrimination](https://mlsanity.com/paper/2605.22081/) (2026)
- [An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution](https://mlsanity.com/paper/2610.15795/) (2026)

## Abstract (arXiv)

Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers use offensive language. Lastly, we conduct many experiments to produce strong results (F1 = 83.2) on the dataset using SOTA techniques.