The growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model’s decision-making mechanisms.

社交网络的发展为持有偏见观点的个人提供了一个平台，使他们能够传播仇恨言论并根据性别、种族、宗教或性取向针对他人。虽然不同社区内的积极互动可以大大增强信心，但至关重要的是要认识到负面评论可能会损害人们的声誉和福祉。这种现象强调需要对这些平台进行更勤奋的监控和强有力的政策，以保护个人免受此类歧视和有害行为的侵害。仇恨言论通常被描述为针对特定群体的故意侵略行为，通常旨在基于他们身份的某些方面来伤害或边缘化他们。大多数与仇恨言论相关的研究都是用英语、西班牙语和法语等资源感知语言进行的。然而，资源匮乏的欧洲语言，如爱尔兰语、挪威语、葡萄牙语、波兰语、斯洛伐克语和许多南亚语，由于语言资源有限而面临挑战，使得信息提取成为劳动密集型。在这项研究中，我们提出了具有 FastText 词嵌入的深度神经网络，使用正则化方法进行挪威语的多类仇恨语音检测，并实现了具有超参数调整和生成配置的基于多语言 Transformer 的模型。当与双向 LSTM 和 GRU 堆叠时，FastText 的性能优于其他深度学习模型，从而形成了 FAST-RNN 模型。在结论阶段，我们将我们的结果与最先进的结果进行比较，并使用局部可解释模型不可知的解释来执行可解释性建模，以更全面地理解模型的决策机制。