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Deep Learning and NLP Methods for Automated Hate Speech Detection Across Human and Machine-Generated Content
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Author(s): Anushree Kangoo (Manipal University Jaipur, India), Vibhakar Pathak (Arya College of Engineering and Information Technology, India), Rohit Mittal (Manipal University Jaipur, India)and Ahmed Antwi-Boampong (Department of Information Technology, Ghana Communication Technology University, Accra, Ghana)
Copyright: 2026
Pages: 16
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.ch012
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Abstract
Hate speech is a widespread and ever-changing issue that poses a serious threat to the safety and dignity of individuals and communities around the globe. This chapter offers a thorough look at hate speech, starting with what it is and how it can show up in different forms—whether in private conversations, public settings, or online spaces. It also addresses tactics like denial and trivialization. A significant focus is given to the alarming increase of hate speech on social media platforms, breaking down key areas such as online harassment, hurtful comments, unfair treatment, and trolling. The chapter discuss challenges such as the ambiguity of language, cultural contexts, and the specific nuances of different platforms. It explores modern detection techniques, particularly highlighting the transformative impact of Natural Language Processing (NLP). The chapter examines the stages of NLP and how it can be applied to understand and identify hate speech, showcasing how linguistic and computational tools can effectively work together to tackle digital toxicity.
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