Hate Speech Detection on Multiple Social Networks Using Deep Learning and Optimization Techniques: A Hybrid Approach

The use of social media networks as a source of hate speech is another emerging factor that complicates the possibility of a comprehensive organization of an environment suitable for promoting healthy communication. Automating the detection of hate speech in various social media networks has turned out to be a very difficult process. It is critical to identify and monitor hate speech to reduce its negative effects on people and groups. Currently, there are many approaches to classifying hate speech, but they still have indeterminacy when it comes to distinguishing between hate and normal messages and low accuracy. Many domains have greatly benefited from deep learning, especially in speech and NLP tasks. The hyperparameters of Deep Neural Networks (DNN) play a crucial role and are reflected in their success. However, because these hyperparameters are highly recursive, it is sometimes difficult to set them for machine learning models, such as deep neural networks. The work proposed in this study employed the sparrow search algorithm (SSA) optimization methods to fine-tune the hyperparameters of deep learning models for hate speech detection. In the training process of the SSA-DNN model, the SSA can help search and select the best hyperparameters. Based on the obtained experimental outcomes, it can be observed that the proposed SSA-DNN model outperforms different machine learning and deep learning techniques in the context of hate speech detection.