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Machine Learning Models for Automated Hate Speech Detection in Synthetic Content
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Author(s): Tarni Khatri (Manipal University Jaipur, India), Vibhakar Pathak (Arya College of Engineering and Information Technology, India), Rohit Mittal (Manipal University Jaipur, India)and Manish Mittal (Brainware University, Kolkata, India)
Copyright: 2026
Pages: 12
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.ch014
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Abstract
This chapter focuses on how Machine Learning (ML) can help identify and reduce hate speech on the internet. With the growing use of Artificial Intelligence (AI), there is a risk that these systems can unintentionally spread or support hate speech. This often happens because the data used to train these models may contain biased or harmful language, and the models may not fully understand the context in which words are used. The chapter explains how different ML techniques—such as supervised learning (where the model learns from labelled examples), unsupervised learning (where the model finds patterns in data without labels) can be used to detect hate speech in text. The chapter also discusses how we can measure the performance of hate speech detection models using metrics such as precision (how many detected hate speech examples were actually hate speech), recall (how many real hate speech examples were correctly found), F1-score (a balance between precision and recall), and ROC-AUC.
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