# OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach

arXiv: https://arxiv.org/abs/2005.07297 · HTML: https://mlsanity.com/paper/2005.07297/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 11 of 11)

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- [CulturePark: Boosting Cross-cultural Understanding in Large Language Models](https://mlsanity.com/paper/2405.15145/) (2024)
- [Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech](https://mlsanity.com/paper/2201.06723/) (2022)
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- [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://mlsanity.com/paper/2410.15308/) (2024)
- [AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify](https://mlsanity.com/paper/2207.08557/) (2022)
- [Transfer Learning Approach for Arabic Offensive Language Detection System -- BERT-Based Model](https://mlsanity.com/paper/2102.05708/) (2021)
- [Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model](https://mlsanity.com/paper/2203.03542/) (2022)
- [Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets](https://mlsanity.com/paper/2407.02448/) (2024)
- [Multi-task Learning with Active Learning for Arabic Offensive Speech Detection](https://mlsanity.com/paper/2506.02753/) (2025)

## Abstract (arXiv)

The preprocessing phase is one of the key phases within the text classification pipeline. This study aims at investigating the impact of the preprocessing phase on text classification, specifically on offensive language and hate speech classification for Arabic text. The Arabic language used in social media is informal and written using Arabic dialects, which makes the text classification task very complex. Preprocessing helps in dimensionality reduction and removing useless content. We apply intensive preprocessing techniques to the dataset before processing it further and feeding it into the classification model. An intensive preprocessing-based approach demonstrates its significant impact on offensive language detection and hate speech detection shared tasks of the fourth workshop on Open-Source Arabic Corpora and Corpora Processing Tools (OSACT). Our team wins the third place (3rd) in the Sub-Task A Offensive Language Detection division and wins the first place (1st) in the Sub-Task B Hate Speech Detection division, with an F1 score of 89% and 95%, respectively, by providing the state-of-the-art performance in terms of F1, accuracy, recall, and precision for Arabic hate speech detection.