对比38种模型，找到高效准确的仇恨言论检测方案。
Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers
- 综合评估传统方法与深度学习模型在文本分类中的表现。
- RoBERTa等变压器模型准确率超90%，而CatBoost等仅需低资源。
- 数据集大小和预处理方式显著影响模型效果，非越大越好。
社交媒体上仇恨言论泛滥，亟需兼顾准确率与计算效率的自动化检测系统。本研究在样本量从6.5K到451K不等的数据集上，评估了38种模型配置，涵盖变压器架构（如BERT、RoBERTa、Distil-BERT）、深度神经网络（如CNN、LSTM、GRU、Hierarchical Attention Networks）以及传统机器学习方法（如SVM、CatBoost、Random Forest）。结果表明，变压器模型，尤其是RoBERTa，在准确率和F1分数上均超过90%，表现最优；深度学习中，层次注意力网络效果最佳；而传统方法如CatBoost和SVM虽性能稍逊，但计算成本极低，F1分数仍高于88%。此外，分析发现平衡且中等规模的原始数据集比更大但已预处理的数据集表现更好。研究为构建高效、可靠的仇恨言论检测系统提供了重要参考。
原文摘要 · Abstract (English)
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.
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