Explainable Identification of Hate Speech towards Islam using Graph Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 2 Nov 2023.
Builds on
18 of 40 references.
References · 18
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- 2023Explaining How Transformers Use Context to Build Predictionscited by 43
- 2020VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classificationcited by 35
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- 2023Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Contextcited by 8
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.