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Offensive Language Detection from Arabic Texts

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Intelligent Computing (SAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1018))

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Abstract

Detecting offensive language (OL) in social media platforms has become an important task for researchers in natural language processing and understanding. Despite emerging research and efforts to address this problem in many languages, more efforts are still needed to improve the performance of OL detection in Arabic-language contexts. This work investigates the state of the art for both the English and Arabic languages, studies the research communities’ different proposed approaches, and compares their performances. We present new approaches to the use of word-embedding models, where each word has two representations: the first representation describes the target word’s context in offensive texts, while the second represents the context of the word in non-offensive texts. The primary results are promising, with a precision reaching an average of 65% of the detection of OL and around 71% for the identification of non-offensive language.

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Acknowledgments

This research work was developed during the sabbatical leave I had from Princess Sumaya University for Technology for the year 2021–2022.

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Authors and Affiliations

  1. King Hussein School for Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

    Arafat A. Awajan

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  1. Arafat A. Awajan
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Correspondence to Arafat A. Awajan .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Awajan, A.A. (2024). Offensive Language Detection from Arabic Texts. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-62269-4_6

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