Abstract
The main goals of this project are to develop reliable hate speech identification models that can recognize derogatory terminology, prejudiced attitudes, and damaging stereotypes on a variety of internet platforms. To help the models train and generalize efficiently, the study focuses on using big datasets with a variety of content types, including both hate speech and non-hate speech. The findings of this study suggest that machine learning has the potential to mitigate the negative consequences of hate speech by using automated filtering and flagging tools. The study also highlights the need for continued research and development to improve the accuracy, uniformity, and transparency of hate speech detection systems, and ultimately to foster a safer online environment to encourage all people.
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