Accuracy Measure for Identification of Fake Blog Articles in Social Website Using Random Forest Over Naive Bayesian Classifier
Sai A. Krishna, G Divya · ECS Transactions · 2022
Aim: To identify fake blog articles that are posted in social media and avoid spreading of fake news in social websites using Random Forest over Naive Bayesian Classifier. Materials and Methods: Fake news is identified using Novel Random Forest (n=3) and Naive Bayesian Classifier (n=3). The performances of the classifiers are evaluated based on their accuracy rate using twitter media dataset. Results and Discussion: The accuracies of Random Forest (99.07%) and Naive Bayesian Classifier (89.40%) and statistical significance (p=0.00) are identified. Conclusion: Random Forest produces significantly better accuracy value than the Naive Bayesian Classifier techniques.