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Exploring Hate Speech Classification in Low-Resource Languages: A Comprehensive Review
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Author(s): Sargam Yadav (Dundalk Institute of Technology, Ireland), Abhishek Kaushik (Dundalk Institute of Technology, Ireland)and Kevin McDaid (Dundalk Institute of Technology, Ireland)
Copyright: 2026
Pages: 46
Source title:
Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations
Source Author(s)/Editor(s): Mohammad Arsalan (Qatar University, Qatar), Mehul Mahrishi (Swami Keshvanand Institute of Technology, India), Ruchi Doshi (Universidad Azteca, Chalco, Mexico), Archika Jain (Swami Keshvanand Institute of Technology, India)and Chandrashekhar Goswami (Sir Padampat Singhania University, India)
DOI: 10.4018/979-8-3373-3063-1.ch008
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Abstract
The widespread adoption of social media has profoundly impacted the lives of individuals, businesses, and governments, fostering greater connectivity. However, a byproduct of online anonymity is that individuals are more willing to display aggression, which can negatively impact groups and discourage their participation. This chapter surveys recent and pertinent literature that utilizes Natural Language Processing (NLP) techniques to automatically detect online hate speech, including studies that explore model explainability with Explainable Artificial Intelligence (XAI), evaluate model fairness, and measure unintended bias. Furthermore, the chapter reviews shared tasks and challenges that have contributed towards advancing research in hate speech detection. The findings suggest that despite the recent strides made in the development of Artificial Intelligence (AI) models for hate speech detection, the problem requires further examination, creation of benchmark datasets, and examination of explainable methodologies, particularly in identification of misogynistic content.
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