Detection of Hate Speech with Attention-Enhanced Deep Learning Techniques

One of the pressing issues on user-generated platforms and social media is hate speech, a threat to online communities. Conventional detection methods, such as keyword matching or basic classification, often miss hate speech's subtleties and contextual dependencies. This paper introduces a pioneering deep learning approach, leveraging transformer architectures and attention mechanisms, to advance the state-of-the-art in hate speech detection. Our model's unique feature is its ability to focus on words that carry more contextual relevance, thereby interpreting the intended meaning of the words more accurately. The multi-head self-attention mechanism allows for differential weighting of various input parts, thereby enhancing the model's ability to distinguish between hostile and hateful language, even in cases of word similarity. We explain the architecture of our model with further explanation of the calculation involved in queries, keys, and values, followed by a description of the mathematical foundation of the attention mechanism. In fine-tuning the pre-trained transformer model, say BERT, from the hate speech dataset, our optimised model uses the Adam optimiser. We run experiments to check the performance of the proposed model by using accuracy, precision, recall, and F1-score to prove that our proposed model outperforms the base models like CNNs and RNNs. Our attention-based approach successfully highlights hate speech in subtle, context-sensitive ways. As this is one of the first studies where attention has been explored in the context of hate speech detection, it provides a good platform for further work on hate speech and multimodal inputs in many other languages. The results of our experiments indicate the promise of attention-based models as a powerful and scalable solution to the real-world challenges of hate speech detection.