RoBERTa-GCN: A Novel Approach for Combating Fake News in Bangla Using Advanced Language Processing and Graph Convolutional Networks

In this era of widespread information, combating fake news in less commonly represented languages like Bengali is a significant challenge. Fake news is a critical issue in Bangla, a language that a vast population uses but lacks adequate natural language processing tools. To address this, our research introduces RoBERTa-GCN, a cutting-edge model combining RoBERTa with a graph neural network (GCN) to accurately identify fake news in Bangladesh. The dataset we utilized comprises articles from 22 prominent Bangladeshi news portals covering diverse subjects such as politics, sports, economy, and entertainment. This comprehensive dataset enables the model to learn and adapt to the intricacies of the Bangla language and its news ecosystem, facilitating effective fake news detection across various content categories. Our approach integrates the RoBERTa model, adapted for Bangla, with GCN's expertise in processing relational data, forming an effective means to differentiate between authentic and fake news. This study's key achievement is the creation and application of the RoBERTa-GCN model to the Bangla language, an area not thoroughly explored in previous research. The findings show that RoBERTa-GCN surpasses existing methods, achieving impressive accuracy rates of 98.60%, highlighting its capability as a robust model for preserving news integrity in the digital era, especially for the Bangla-speaking population.