Context-Aware Hate Speech Detection using Transformer-based Models (BERT) for Social Media Text Analysis

The convergence of machine learning and natural language processing offers a transformative approach to addressing the growing challenge of online hate speech detection. Traditional computational techniques often struggle with the complexities of unstructured textual data and linguistic nuances. This study introduces a machine learning-based framework for hate speech recognition, leveraging advanced algorithms and data preprocessing techniques to enhance detection accuracy. The proposed approach demonstrates significant improvements in performance, particularly in handling imbalanced datasets and underscoring the potential of machine learning as a robust solution for moderating harmful content.