Fake News Detection of Social Media News in Block Chain Framework Using Machine Learning
Raghunath Singh, Aarfa Rajput, Wakeel Ahmed, Vaishali Kaushik, Rashid Ansari, Manish Sachan · 2025
The spread of social media with fake news is quite serious as it destroys trust, creating more misinformation. A research on this topic, introduces a blockchain-based framework, which integrates supervised algorithms to detect and mitigate the effects of fake news through its three core modules: data publisher, analytical model, and data provider. Once such processes including hash generation, transaction mining, smart contract creation, and peer-to-peer consensus get initiated, the news pieces uploaded by the anonymous are stored safely in the forms of blockchain blocks. Utilizing natural language processing algorithms to extract features and classify within it, the Analytical Model also checks for its truthworthiness by verifying the extracted data from the blockchain dataset by the Data Provider module which further enhances the accuracy. The framework was tested on the LIAR dataset using a distributed platform for analyzing the validation, insertion, and retrieval times that were found to increase linearly with the growth in data size. Additional tests for consensus validation across nodes also showed the system was scalable and reliable. This leads to an assumption that blockchain and ML are together the best to deal with the spread of false information on social media, guaranteeing that trustworthy news reaches readers.