Fake Media Detection Based on Natural Language Processing and Blockchain Approaches

The rise of fake news on social media platforms poses a significant threat to the credibility of news content. In this proposed paper, the author introduces an advanced approach for fake media detection, leveraging Reinforcement Deep Learning algorithms and Blockchain technology. The Reinforcement algorithm, trained on Natural Language Processing (NLP) techniques, evaluates news similarity, distinguishing between genuine and fake news. The use of Blockchain ensures the security and integrity of news data by employing a decentralized storage model. Each news article is stored as a unique block with a hashcode, providing tamper-proof and transparent storage across multiple nodes. The integration of NLTK techniques for text preprocessing enhances the accuracy of fake news detection. This innovative approach addresses the limitations of traditional algorithms and introduces a robust, secure, and authorization-enabled system for media integrity.

Keywords:

Fake Media Detection, Reinforcement Deep Learning, Blockchain, Natural Language Processing, NLTK, Decentralized Storage, Tamper-Proof, Text Preprocessing.