Hybrid Model for Fake News Detection with Blockchain and Bidirectional Transformer

Abstract

The consequences of the spread of fake news, particularly in today’s digital world, are serious. Today’s fake news rapid cycle affects public opinion and creates a misinformation effect. Traditional fact-checking methodologies are slow, inaccurate, and manipulatable, limiting our control over the rapidly generated false information cycle. This project proposes an intelligent and safe solution to addressing fake news through the leverage of Blockchain technology with the use of Natural Language Processing (NLP). At the center of BIO is BERT (Bidirectional Encoder Representations from Transformers), a cutting-edge model of deep learning that allows for an understanding of both the context and meaning behind words. This allows BERT to identify subtle nuances of misinformation that older processes have trouble identifying. To record verified news articles in the Blockchain produces a secure, tamperproof, decentralized model with integrity so that news articles cannot be changed post publication. This is a two-pronged effect that will also build confidence by verifying the credibility of the news sources of information. By marrying cutting-edge artificial intelligence and secure data storage, this project is positioned to provide a scalable, reliable and minimal data input method to combat fake news and misinformation while promoting a trustworthy information ecosystem to consumers by offering better source credibility.