<![CDATA[This paper addresses the critical issue of hate speech detection in social media, a growing concern given the widespread use of online platforms for communication and information dissemination. The proliferation of hate speech contributes to online harassment, discrimination, and the propagation of harmful ideologies, posing significant societal challenges. This study proposes a machine learning-based approach for identifying and classifying hate speech across various social media datasets. We leverage a comprehensive collection of parsed datasets, including those related to aggression, attack, toxicity, and specific instances from Twitter (general, racism, sexism), YouTube, and Kaggle. The methodology involves data preprocessing, feature extraction, and the application of machine learning algorithms to effectively distinguish hate speech from benign content. Our findings aim to contribute to the development of robust automated systems for content moderation, fostering safer and more inclusive online environments.]]>
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