PREDICTING FAKE INFORMATION IN SOCIAL NETWORK AND AUTHENTICATION USING BLOCK CHAIN
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PREDICTING FAKE INFORMATION IN SOCIAL NETWORK AND AUTHENTICATION USING BLOCK CHAIN
Authors:
M Narender1, Mohammad Saqlain Danish2, M Sandeep Reddy3 , K Narendra4, M Sai Kumar Chary5
1-5 Department of CSE & TKR College of Engineering & Technology
2-5cB.Tech Students
ABSTRACT: Social media's rapid expansion has resulted in a startling increase in the dissemination of false information, endangering public confidence, security, and opinion. This project suggests a hybrid strategy that successfully detects and tracks fake news in social networks by fusing blockchain technology with machine learning. We improve detection accuracy by using a multi-classifier ensemble model that consists of Random Forest, Decision Tree, and Passive Aggressive classifiers. These models are combined in a voting classifier to provide reliable predictions. The system uses blockchain for user authentication and fake news traceability to guarantee transparency and immutability. The implementation uses a two-module architecture: the Participant Node Module, which controls news content, and the Verifier Node Module, which deals with user registration and verification.
Keywords — Block Chian, Machine Learning, Random Forest, Decision Tree, Passive Aggressive Classifier.
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