Exploring Learning Techniques in Language Models for Classifying Hate and Offensive Speech in Portuguese

Abstract

 Social Media platforms, significant in modern debate and communication, face the challenge of managing a vast and disorderly volume of hateful content and disinformation. This work examines the detection of hate speech in Portuguese, contemplating its unique linguistic and cultural nuance. Leveraging Transformer-based models and different training and activation strategies, nine models with variations in architecture, size, and pre-training corpora are evaluated. Our findings show that, even though large generative models with enhanced prompts exhibited promising results, tuned small language models remain superior in addressing this task.