Abstract
Any statement that is vituperative towards an individual or a group based on their traits like race, ethnicity, gender, sexual orientation, color, religion, nationality, or another attribute is described as hate speech. Hate speech and bullying, spreading uncontrolled might undermine society’s peace and harmony, becoming a societal issue. Especially when hate speech is used to hurt people or to hurt the respect of individuals, groups, or countries. This complicates the task since social media posts contain paralinguistic tools (e.g., emoticons and hash tags) and a lot of poor quality written text that does not follow grammatical norms. With the recent advancements in NLP, it is possible to analyze unstructured composite natural language content. The chapter first focuses on discussing various deep learning architectures such as DCNNs, Bi- LSTMs, Transformers and models like BERT and how they are applied in identifying hate speech in social media. The chapter examines the capacity of deep learning algorithms to capture hate speech on public media systematically. The chapter also reviews the accuracy of models on publicly available standard datasets. The findings of this study pave the way for more research into the discovery of spontaneous abusive conduct on social media in the future.
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Gudumotu, C.E., Nukala, S.R., Reddy, K., Konduri, A., Gireesh, C. (2023). A Survey on Deep Learning Models to Detect Hate Speech and Bullying in Social Media. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_2
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