Accuracy measure for identification of fake blog articles in social website using random forest over support vector machine
C.S. Muralidhara Krishna, G. Divya · AIP conference proceedings · 2024
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 Support Vector Machine.Materials and Methods: Fake news is identified using Novel Random Forest (n=3) and Support Vector Machine (n=3).The Performances of the classifiers are evaluated based on their accuracy rate using twitter media dataset.Results and Discussion : The accuracy of Random Forest (99.06%) and Support Vector Machine (98.72%) is obtained.There was a statistical significance between Novel Random Forest and Support Vector machine(p=0.00)are identified .Conclusion : Novel Random Forest produces significantly better accuracy value than the Support Vector Machine