Abstract
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.
Abstract (translated)
社交媒体平台上仇恨言论的泛滥促使开发有效的检测和管理工具。本研究评估了各种机器学习模型在识别仇恨言论和冒犯性语言方面的有效性，并探讨了文本转换技术消除此类内容的潜力。我们比较了传统的CNNs（卷积神经网络）和LSTMs（长短时记忆网络）模型与先进的神经网络模型如BERT及其衍生产品，同时探索结合不同架构特性的混合模型。我们的研究结果表明，虽然像BERT这样的先进模型由于其深刻的语境理解在准确性方面表现出色，但混合模型在某些情况下展示了改进的能力。此外，我们引入了创新的文本转换方法，将负面表达转化为中立表述，从而可能减轻有害内容的影响。文中讨论了这些发现的意义，并强调了当前技术的优势和局限性，并提出了未来构建更强大的仇恨言论检测系统的方向。
URL
https://arxiv.org/abs/2602.20634
