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Detecting Hate Speech in the Digital Age: AI Solutions for Real-Time Moderation
Abstract
This chapter explores the application of artificial intelligence (AI) in real-time hate speech detection on social media platforms, aiming to address the limitations of traditional moderation techniques. Utilizing machine learning frameworks such as TensorFlow and PyTorch, the study examines the effectiveness of AI models in processing vast amounts of data to identify nuanced forms of hate speech. Key findings indicate that while AI can significantly enhance detection speed and accuracy, challenges remain regarding bias in training datasets and the need for human oversight to ensure ethical moderation practices. The implications of this research highlight the necessity for a balanced approach that integrates AI capabilities with human judgment to foster safer online environments while preserving free speech. This study contributes to ongoing discussions about the ethical deployment of AI technologies in content moderation.
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