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Hate speech spreads as the quantity of content on the internet grows. Automated methods for identifying hate speech in text face a number of challenges, which we investigate and assess. Language complexity, differing views on what constitutes hate speech, and data availability restrictions for algorithm training and testing are some of the difficulties. As a result, it may be difficult to decipher the reasoning behind the decisions made by many current methods. With our multiview SVM technique, we give near-state of the art SVM results that are easier to comprehend than neural approaches. In addition, we look at the challenges that this endeavour faces on a technological and practical level.
Pawar et al. (Thu,) studied this question.