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<p>This study addresses the critical gap in detecting offensive language within Kuwaiti Arabic (KA), a dialect extensively used in informal digital communication yet poorly supported by existing Natural Language Processing (NLP) systems. Given the increasing prevalence of abusive online content that threatens cybersecurity, safety, and social cohesion, the development of dialect-sensitive detection models is essential. Building upon prior work, this research constructs the first Kuwaiti offensive language data set by annotating an existing corpus, enabling the development of a specialized text classification system tailored to KA. The proposed system not only aims to accurately identify offensive content but also integrates sentiment analysis to explore the relationship between emotional expression and offensive language. Additionally, a detailed empathetic lexicon-based analysis examines linguistic patterns and emotional triggers that drive offensive communication in KA online discourse. The work includes comprehensive model evaluation, misclassification analysis, and deployment of a web-based demo. We also carried out an error analysis using both manual inspection and explainable AI approaches such as SHapley Additive exPlanations (SHAP). The findings underscore the importance of dialect-specific NLP resources for safeguarding digital spaces and enhancing cybersecurity strategies, while also contributing to a deeper understanding of the linguistic and emotional dynamics that underpin offensive language in Kuwaiti social media interactions.</p>

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Towards safe digital communication: building an offensive language detection framework for Kuwaiti Arabic

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月8日

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  • Fatemah Husain

摘要

This study addresses the critical gap in detecting offensive language within Kuwaiti Arabic (KA), a dialect extensively used in informal digital communication yet poorly supported by existing Natural Language Processing (NLP) systems. Given the increasing prevalence of abusive online content that threatens cybersecurity, safety, and social cohesion, the development of dialect-sensitive detection models is essential. Building upon prior work, this research constructs the first Kuwaiti offensive language data set by annotating an existing corpus, enabling the development of a specialized text classification system tailored to KA. The proposed system not only aims to accurately identify offensive content but also integrates sentiment analysis to explore the relationship between emotional expression and offensive language. Additionally, a detailed empathetic lexicon-based analysis examines linguistic patterns and emotional triggers that drive offensive communication in KA online discourse. The work includes comprehensive model evaluation, misclassification analysis, and deployment of a web-based demo. We also carried out an error analysis using both manual inspection and explainable AI approaches such as SHapley Additive exPlanations (SHAP). The findings underscore the importance of dialect-specific NLP resources for safeguarding digital spaces and enhancing cybersecurity strategies, while also contributing to a deeper understanding of the linguistic and emotional dynamics that underpin offensive language in Kuwaiti social media interactions.