The proposed XG-HSI framework, which utilizes Graph Neural Networks (GNNs) for the detection and explanation of hate speech towards Islam, can be effectively extended to target other marginalized communities by following a systematic approach. First, the framework can be adapted to focus on specific datasets that contain hate speech directed at various groups, such as racial minorities, LGBTQ+ individuals, or religious sects. This involves curating or creating datasets similar to the HateXplain dataset, ensuring that they are rich in context and labeled accurately for the targeted community.
Next, the preprocessing steps in the XG-HSI framework can be modified to include community-specific linguistic features and cultural nuances. This may involve employing domain-specific pretrained NLP models that are fine-tuned on texts relevant to the targeted community, thereby enhancing the contextual understanding of hate speech.
Furthermore, the graph construction process can be tailored to reflect the unique relationships and interactions within the new community. By establishing edges based on contextual similarities and community-specific language patterns, the GNN can better capture the dynamics of hate speech propagation within that community.
Finally, the explainability aspect of the framework can be enhanced by incorporating community feedback and expert insights during the model training and evaluation phases. This participatory approach ensures that the explanations generated by the GNN are not only technically sound but also resonate with the lived experiences of the targeted community, thereby improving the model's interpretability and acceptance.