对比多种模型与文本转化技术，提升社交媒体仇恨言论检测效果。
Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
- 用BERT等模型结合文本转化技术识别仇恨言论。
- 混合模型在特定场景下表现优于单一先进模型。
- 适合研究内容安全与算法优化的从业者参考。
社交媒体上仇恨言论的泛滥催生了高效检测与管理工具的需求。本研究评估了多种机器学习模型在识别仇恨言论和攻击性语言方面的有效性，并探索了文本转换技术中和有害内容的潜力。我们比较了CNN、LSTM等传统模型与BERT及其衍生模型，以及融合不同架构特征的混合模型。结果表明，尽管BERT等先进模型凭借深层上下文理解取得更高准确率，但混合模型在某些场景下展现出更优能力。此外，我们提出了创新的文本转换方法，将负面表达转为中性表述，从而减轻有害内容的影响。研究讨论了现有技术的优势与局限，提出未来构建更鲁棒仇恨言论检测系统的方向。
原文摘要 · Abstract (English)
The proliferation of hate speech on social media platforms has necessitated the development of effective detection and moderation tools. This study evaluates the efficacy of various machine learning models in identifying hate speech and offensive language and investigates the potential of text transformation techniques to neutralize such content. We compare traditional models like CNNs and LSTMs with advanced neural network models such as BERT and its derivatives, alongside exploring hybrid models that combine different architectural features. Our results indicate that while advanced models like BERT show superior accuracy due to their deep contextual understanding, hybrid models exhibit improved capabilities in certain scenarios. Furthermore, we introduce innovative text transformation approaches that convert negative expressions into neutral ones, thereby potentially mitigating the impact of harmful content. The implications of these findings are discussed, highlighting the strengths and limitations of current technologies and proposing future directions for more robust hate speech detection systems.
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