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Real-Time Hate Speech Detection API: A Scalable Deep Learning Approach
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Author(s): Vikash Deep Bhaskar (Madan Mohan Malaviya University of Technology, India)and Shagufta Shakeel (Madan Mohan Malaviya University of Technology, India)
Copyright: 2026
Pages: 32
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.ch009
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Abstract
The rise of digital communication has exacerbated the challenge of tackling harmful speech online, particularly hate speech, which dehumanizes individuals or groups based on traits such as race, gender, or ethnicity. Hate speech has been an issue since the start of the Internet, but the advent of social media has brought it to unimaginable heights. To address such an important issue and to improve contextual understanding and classification accuracy, this paper presents a new, scalable, and high-performance method for implementing a real-time hate speech detection API that uses the T5 transformer model to effectively detect highly discussed topics that generate hate speech on twitter. The system uses a deep learning approach, T5EncoderModel, to detect hate speech on annotated tweets. It integrates the T5 model with a dense classification head and is trained using PyTorch. It is deployed via FastAPI to enable real-time classification of social media content. Evaluation demonstrates that our approach outperforms existing models.
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