Explainable Identification of Hate Speech towards Islam using Graph Neural Networks (2024)

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.

## introduction detecting and eliminating hate speech on social media platforms is of utmost importance for the promotion of harmony and tranquillity in society @xcite @xcite . the escalating presence of hate speech specifically targeting islam or muslim communities on online discussion platforms is a growing concern @xcite . this form of hate speech not only fosters an environment of intolerance and hostility but can also have severe psychological impacts on individuals and communities, leading to real-world violence and discrimination @xcite . to address this issue, researchers have increasingly turned to advanced technologies; using textprocessing approaches in ai. natural language processing (nlp) techniques are frequently employed for hate speech detection, with some offering severity assessment of hate speech (? @xcite @xcite . these methods utilize sophisticated algorithms to analyse vast amounts of textual data, identifying patterns and features indicative of hate speech. for instance, deep learning models, like recurrent neural networks (rnns), can learn complex representations of text data, enabling them to detect subtle and context-dependent instances of hate speech @xcite . modern nlp techniques, on the other hand, can enhance these models by providing richer linguistic insights. tokenization, part-of-speech tagging, and named entity recognition are just a few nlp techniques that help in breaking down and understanding the text's structure and meaning. moreover, the integration of latest nlp model and transformers, like bert @xcite and gpt @xcite , has significantly improved the ability of models to understand context @xcite , sarcasm @xcite , and implicit hate speech @xcite , which are often challenging to detect. another interesting approach is to use human-centric perspectives of ai using some benchmark dataset @xcite . researchers have tried to employ gnns in hate speech classification @xcite @xcite , but still needs more focus on this area. despite their potential, gnns have not been actively employed for the purpose of interpretable identification of hate speech, particularly in islamic contexts. islamophobic 1 content often exhibits close word choices and hate speakers from the same community, which gnns can leverage to reveal and explain patterns, alongside impressive classification scores. in this study, we introduce a novel approach employing graph neural networks for the identification and explication of hate speech directed at islam (xg-hsi), as demonstrated in figure 2 . we pre-process the dataset to focus on islamic contexts, utilize pretrained nlp models for word embeddings, establish connections between texts, and employ a series of graph encoders for hate speech target identification, which achieves state-of-the-art performance. ## background graph neural networks (gnns) are powerful neural networks designed for processing non-euclidean 2 data organized in complex, interconnected graphs @xcite . using their ability to utilize relations between different data points @xcite , gnns have shown tremendous promise in 1 in this work, the terms "hate speech towards islam" and "islamophobic hate speech" is used interchagably. in this work, we adopted a general bag of wordsbased approach to create graphs, without llms. by integrating with pretrained nlp models, gnns leverage contextual word embeddings to better understand the subtleties of hate speech. this combined approach improves the accuracy, contextawareness, and adaptability of detection systems, making them more effective in identifying hate speech directed at islam and potentially generalizing to other targeted groups. ## graph explanation case study for a given post, "how is all that awesome muslim diversity going for you native germans? you have allowed this yourselves. if you do not stand and fight against this. you get what you asked for what you deserve!", the predicted classification was offensive towards islam. as per the explainer (figure 4 ), the neighbouring and self-tokens foot_2 helped to classify this as offensive to muslims are fight, muslim diversity, brooks, ##rish, donald, syrian, schultz, typed. the text's association of "muslim diversity" with potential blame and its confrontational tone in phrases like "stand and fight against this," combined with neighbouring tokens like syrians, brooks, syrians denoted negative sentiment. more detailed analysis is added in appendix a. ## discussion we believe that our study not only addresses the immediate challenge of identifying and explaining hate speech directed at islam but also recognizes the broader impact of hate speech propagation on online platforms. the proliferation of islamophobic language fosters intolerance, division, and hostility within communities, perpetuating harmful stereotypes and prejudices. by leveraging gnns in our xg-hsi framework, we not only detect hate speech but also provide explanations for its occurrence, shedding light on the underlying factors driving such behaviour. gnns excel in capturing complex relationships and patterns within data, enabling them to effectively identify instances of hate speech and elucidate the contextual nuances surrounding them. by leveraging the inherent structure of social networks and textual data, our approach offers a comprehensive understanding of how hate speech propagates in online discourse. in future research, exploring the integration of multimodal data sources, such as images and videos, could enhance the robustness of hate speech detection models, particularly in detecting nuanced forms of islamophobic content. additionally, investigating the dynamic nature of online communities and incorporating temporal aspects into gnn architectures could provide deeper insights into the evolution of hate speech propagation and enable more proactive interventions to counter its spread. ## conclusion identifying and addressing islamophobic hatred on social media is crucial for achieving harmony and peace. this research presents a novel method using gnns to detect hate speech towards islam. empirical findings demonstrate that our model achieves exceptional performance, significantly outperforming all others, with xg-hsi-bert achieving the highest accuracy (0.741) and macro f1 (0.747). explainability aspect of this approach is also very promising, as it provides insights into both correlations and causation. this further highlights the potential of gnns in combating online hate speech and fostering a safer, more inclusive online environment.