BGPCN: A BERT and GPT-2-based Relational Graph Convolutional Network for hostile Hindi information detection

The proliferation of hostile content on social media platforms, particularly in low-resource languages such as Hindi, poses significant challenges to maintaining a safe online environment. This study introduces the BGPCN model, which leverages the strengths of Bidirectional Encoder Representations from Transformers (BERT) & Generative Pre-trained Transformer 2 (GPT-2) embeddings integrated with a Relational Graph Convolutional Network (R-GCN) in order to identify hostile information in the language of Hindi. The model addresses both Coarse-grained (Hostile or Non-Hostile) and Fine-grained (Fake, Defamation, Hate, Offensive) classification tasks. The proposed model is evaluated on the Constraint 2021 Hindi dataset, outperforming the latest methodologies in terms of F1-Score of 0.9816, 0.85, 0.50, 0.62, 0.65 regarding both coarse-grained & fine-grained classifications. Comprehensive error analysis and ablation studies underscore the robustness of the BGPCN model while identifying opportunities for refinement. The findings demonstrate that BGPCN offers a reliable and scalable solution for hostile content detection, with potential applications in social media monitoring and content moderation. The data and code will be publicly accessible in https://github.com/mani-design/BGPCN . • Proposed model detects hostile Hindi content using BERT, GPT-2, and R-GCN. • Proposed model excels in coarse and fine-grained classification with high F1-Scores. • Comprehensive analysis shows the proposed model’s robustness and potential for refinement. • Proposed model sets a new standard for hostile content detection in low-resource languages.