Real-Time Fake News Detection System using LSTM and Blockchain for Tamper-Proof Verification

In this paper, we propose a novel detection of misleading content systems based on Long ShortTerm Memory (LSTM) networks and Transformer models (BERT) for improved text classification. The decentralized ledger system is interconnected for data integrity and tamper-proof verification. VS Code developing the feature and web apps for real-time deployment. To ensure accurate detection, the architecture utilizes state-of-the-art machine learning, while verification is done for reliability over blockchain. The model is trained on a variety of datasets, such as the LIAR dataset as well as custom-built datasets. It provides excellent accuracy, rapid processing, and secure data handling. The integrated system produced a 94% accuracy rate, exceeding standard methods. Integration with blockchain boosted trust and transparency around the detection.