Fake Media Detection Based on Natural Language Processing and Blockchain Approaches

Abstract

Social media networks are critical in the conveyance of data and information in today's digital world. Despite their many benefits, there is one big issue with fake and misinformation news: it exists. Reliable content is vital for the credibility of social media platforms and is a serious threat to social media users who seek factual information. To tackle this problem we present an integrated system using blockchain technology and NLP - enhanced with ML techniques - to successfully detect and fight bogus news. We use Reinforcement Learning to enhance the system for detecting fake user accounts and fake posts. This is more effective for safeguarding the integrity of information distributed through social media networks. We additionally deploy the decentralised blockchain architecture in our approach, increasing the safety of the system and generating undeniable proof of the right of the digital contents. The main goal of this system is to build a safe platform for forecasting and recognising bogus news in order to create a more safe and dependable social media ecosystem. We aim to integrate these technologies in a novel manner which might help alleviate the dissemination of misinformation and help people get accurate, reliable information. “INDEX TERMS: Natural language processing, blockchain, fake media, reinforcement learning.”