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Detecting Hate Speech: Human and AI-Generated Content
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Author(s): Kashvi Chaturvedi (Ajeenkya D.Y. Patil University, India), Yadnyesh Khapekar (Ajeenkya D.Y. Patil University, India), Sunil Sankathala (Ajeenkya D.Y. Patil University, India), Aditya Shrivastav (Ajeenkya D.Y. Patil University, India), Atharva Haresh Saraf (Ajeenkya D.Y. Patil University, India)and Susanta Das (Ajeenkya D.Y. Patil University, India)
Copyright: 2026
Pages: 24
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.ch002
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Abstract
Hate speech refers to language that incites hostility, discrimination, or violence against individuals or groups based on attributes such as race, gender, religion, or sexual orientation. Its detection poses significant challenges due to contextual ambiguity, cultural variance, & linguistic nuance. Artificial intelligence, particularly natural language processing & deep learning models such as CNNs, RNNs, & transformer-based architectures like BERT, have emerged as a critical tool for automating hate speech detection. Word embeddings, contextual modelling, and hybrid detection frameworks that combine human oversight with algorithmic scalability are being actively developed to improve performance. Ethical issues arise around freedom of speech, data privacy, and algorithmic bias. The review highlights techniques for detection, intervention strategies, such as Reflective User Interfaces and content flagging systems, which aim to encourage positive digital behaviour while minimizing harm.
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