Explainable Identification of Hate Speech towards Islam using Graph Neural Networks
Abstract
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.
Cite
@article{arxiv.2311.04916,
title = {Explainable Identification of Hate Speech towards Islam using Graph Neural Networks},
author = {Azmine Toushik Wasi},
journal= {arXiv preprint arXiv:2311.04916},
year = {2024}
}
Comments
Accepted in: (i) NeurIPS 2023 : Muslims in ML Workshop (Non-archival) (https://www.musiml.org/schedule/#:~:text=Azmine%20Toushik%20Wasi) (ii) EMNLP 2024 : NLP for Positive Impact Workshop (Archival; ACL Anthology: https://aclanthology.org/2024.nlp4pi-1.23/)