Islamophobic language is a prevalent challenge on online social interaction platforms. Identifying and eliminating such hatred is a crucial step towards a future of harmony and peace. This study presents a novel paradigm for identifying and explaining hate speech towards Islam using graph neural networks. Utilizing the intrinsic ability of graph neural networks to find, extract, and use relationships across disparate data points, our model consistently achieves outstanding performance while offering explanations for the underlying correlations and causation.

翻译：伊斯兰恐惧语言是在线社交互动平台上的普遍挑战。识别并消除此类仇恨言论是实现和谐与和平未来的关键一步。本研究提出了一种基于图神经网络识别并解释针对伊斯兰仇恨言论的新范式。利用图神经网络发现、提取并利用不同数据点间关系的固有能力，我们的模型持续取得卓越性能，同时为该领域潜在的关联与因果关系提供解释。