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Open AccessArticle
SOD: A Corpus for Saudi Offensive Language Detection Classification
by
Afefa Asiri
Afefa Asiri *
and
Mostafa Saleh
Mostafa Saleh *
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Computers 2024, 13(8), 211; https://doi.org/10.3390/computers13080211 (registering DOI)
Submission received: 9 July 2024
/
Revised: 13 August 2024
/
Accepted: 19 August 2024
/
Published: 20 August 2024
Abstract
Social media platforms like X (formerly known as Twitter) are integral to modern communication, enabling the sharing of news, emotions, and ideas. However, they also facilitate the spread of harmful content, and manual moderation of these platforms is impractical. Automated moderation tools, predominantly developed for English, are insufficient for addressing online offensive language in Arabic, a language rich in dialects and informally used on social media. This gap underscores the need for dedicated, dialect-specific resources. This study introduces the Saudi Offensive Dialectal dataset (SOD), consisting of over 24,000 tweets annotated across three levels: offensive or non-offensive, with offensive tweets further categorized as general insults, hate speech, or sarcasm. A deeper analysis of hate speech identifies subtypes related to sports, religion, politics, race, and violence. A comprehensive descriptive analysis of the SOD is also provided to offer deeper insights into its composition. Using machine learning, traditional deep learning, and transformer-based deep learning models, particularly AraBERT, our research achieves a significant F1-Score of 87% in identifying offensive language. This score improves to 91% with data augmentation techniques addressing dataset imbalances. These results, which surpass many existing studies, demonstrate that a specialized dialectal dataset enhances detection efficacy compared to mixed-language datasets.
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MDPI and ACS Style
Asiri, A.; Saleh, M.
SOD: A Corpus for Saudi Offensive Language Detection Classification. Computers 2024, 13, 211.
https://doi.org/10.3390/computers13080211
AMA Style
Asiri A, Saleh M.
SOD: A Corpus for Saudi Offensive Language Detection Classification. Computers. 2024; 13(8):211.
https://doi.org/10.3390/computers13080211
Chicago/Turabian Style
Asiri, Afefa, and Mostafa Saleh.
2024. "SOD: A Corpus for Saudi Offensive Language Detection Classification" Computers 13, no. 8: 211.
https://doi.org/10.3390/computers13080211
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