Pattern recognition in hate speech detection: a neural network and ensemble approach (Research, Society and Development, 2025)
Hate speech on online platforms is a growing problem with significant social impacts. This work proposes an approach for binary classification of hate speech in Portuguese texts using machine learning and deep learning algorithms. The experiments were conducted on an annotated dataset, with textual representations generated by pre-trained GloVe word embeddings. The voting-based model, which combines the outputs of the base classifiers, achieved the best overall performance, reaching an F1-score of 0.76. The results demonstrate the effectiveness of neural networks, especially in capturing complex textual patterns, and highlight the potential of combined approaches for the hate speech classification task. This study reinforces the importance of exploring diverse architectures and preprocessing techniques tailored to the specific characteristics of the Portuguese language.