Abstract
The emergence of digital and their products, such as Facebook, Twitter, Reddit, Instagram, and other social media entities, has become the dominant medium for exchanging information, connecting people, expressing emotion and feelings, and influencing audiences and communities. Sometimes, these platforms are used by some social media users to engage in inappropriate behavior and expression by using offensive, hateful, and harassing content to express their views and dissatisfaction. Negative, hateful, and abusive content can hurt or harm other social media users and communities and can lead to law and order problems in society. There is a need to stop and mitigate the effects of hate speech by develo** intelligent systems and models to detect them. Artificial Intelligence/machine learning can help in the detection of social media posts, comments, and replies that are hateful, abusive, and offensive. Such content also correlated with race, gender, sexuality, religion, and age. The objective is to evaluate the performance of the proposed model in comparison to other machine learning and deep learning algorithms on the newly collected data. The experimental results demonstrate that the proposed model, CNN with word embeddings, exhibits outstanding performance in hate speech detection. It achieves a remarkable accuracy of 83%. CNN’s word embeddings’ contextual understanding of language and its ability to capture complex semantic relationships contribute significantly to its superior performance compared to other traditional machine learning and deep learning models.
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Kumar, S., Bhagat, A.K., Erugurala, A., Mirza, A., Jha, A.N., Verma, A.K. (2024). A Novel Hybrid Model of Word Embedding and Deep Learning to Identify Hate and Abusive Content on Social Media Platform. In: Farmanbar, M., Tzamtzi, M., Verma, A.K., Chakravorty, A. (eds) Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. FAIEMA 2023. Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications. Springer, Singapore. https://doi.org/10.1007/978-981-99-9836-4_4
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