Islamophobic language on online platforms fosters intolerance, making detection and elimination crucial for promoting harmony. Traditional hate speech detection models rely on NLP techniques like tokenization, part-of-speech tagging, and encoder-decoder models. However, Graph Neural Networks (GNNs), with their ability to utilize relationships between data points, offer more effective detection and greater explainability. In this work, we represent speeches as nodes and connect them with edges based on their context and similarity to develop the graph. This study introduces a novel paradigm using GNNs to identify and explain hate speech towards Islam. Our model leverages GNNs to understand the context and patterns of hate speech by connecting texts via pretrained NLP-generated word embeddings, achieving state-of-the-art performance and enhancing detection accuracy while providing valuable explanations. This highlights the potential of GNNs in combating online hate speech and fostering a safer, more inclusive online environment.

翻译：在线平台上的伊斯兰恐惧症言论助长不容忍情绪，因此检测与消除此类言论对促进社会和谐至关重要。传统仇恨言论检测模型依赖于分词、词性标注和编码器-解码器模型等自然语言处理技术。然而，图神经网络凭借其利用数据点间关系的能力，能够提供更有效的检测和更强的可解释性。本研究将言论表示为节点，并根据其上下文和相似性通过边进行连接以构建图结构。本文提出了一种利用图神经网络识别和解释伊斯兰仇恨言论的新范式。该模型通过预训练自然语言处理生成的词嵌入连接文本，利用图神经网络理解仇恨言论的上下文和模式，在提供有价值解释的同时实现了最先进的检测性能与准确率提升。这凸显了图神经网络在打击网络仇恨言论、构建更安全包容网络环境方面的潜力。