Authors - Hasan Ahmed, Ram Singh Abstract - The growth of digital media platforms has resulted in more disseminated falsehoods which now include elaborate AI-generated syn thetic text instead of manually created false information. The develop ments create major obstacles which disrupt both information trustwor thiness and public confidence. The research presents a High-Accuracy Misinformation Detection Hybrid Transformer Framework which uses BERT and RoBERTa models within an ensemble learning system. The system undergoes initial training on WELFake dataset which serves as a standard benchmark collection that contains equal proportions of au thentic and fraudulent news articles derived from both verified and un verified sources. The framework achieves adaptability through its in cremental updating process which incorporates contemporary headlines and machine-generated content. The weighted fusion mechanism merges probability results from both transformer models to decrease model spe cific bias while strengthening the system’s classification ability. The sys tem shows better results than single transformer setups and operates through a web-based system which provides immediate misinformation assessment. The study results show that using ensemble modeling to gether with scheduled model updates creates an efficient method for tackling the ongoing emergence of synthetic misinformation.
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