A Comparative Estimation of Media Manipulation Disclosure using Machine Learning Algorithms

The suggested method to detect the accuracy level of fake information by applying a sequence of natural language processing (NLP), blockchain, and reinforcement learning approaches. The system applies NLP algorithms to assess the information of social media and their accuracy levels that is assigned a trust score and stored on a blockchain network. Reinforcement learning is used to train the algorithm and create more trust score predictions based on input from the blockchain network. The system is continually updated with new data, reinforcement learning algorithm improves the trust score predictions based on user feedback. The blockchain network maintains the security and immutability of the trust scores and assures that they cannot be tampered. The technology features a user interface that allows the users to verify the accuracy level of news data by verifying their trust rankings on the blockchain network. The technique has the potential to reduce the spread of false news by providing reliable and transparent mechanism to user and verify the accuracy of news data. The implementation of blockchain technology assures the security and integrity of the system, rendering it resistant to manipulation or tampering.