Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers
It addresses the need for efficient automated detection systems for social media platforms, but is incremental as it primarily benchmarks existing methods.
This study tackled the problem of hate speech detection on social media by evaluating 38 model configurations, finding that transformers like RoBERTa achieved accuracy and F1-scores over 90%, while traditional methods like CatBoost and SVM remained competitive with F1-scores above 88% at lower computational costs.
The proliferation of hate speech on social media necessitates automated detection systems that balance accuracy with computational efficiency. This study evaluates 38 model configurations in detecting hate speech across datasets ranging from 6.5K to 451K samples. We analyze transformer architectures (e.g., BERT, RoBERTa, Distil-BERT), deep neural networks (e.g., CNN, LSTM, GRU, Hierarchical Attention Networks), and traditional machine learning methods (e.g., SVM, CatBoost, Random Forest). Our results show that transformers, particularly RoBERTa, consistently achieve superior performance with accuracy and F1-scores exceeding 90%. Among deep learning approaches, Hierarchical Attention Networks yield the best results, while traditional methods like CatBoost and SVM remain competitive, achieving F1-scores above 88% with significantly lower computational costs. Additionally, our analysis highlights the importance of dataset characteristics, with balanced, moderately sized unprocessed datasets outperforming larger, preprocessed datasets. These findings offer valuable insights for developing efficient and effective hate speech detection systems.