Design and analysis of predictive model to detect fake news in online content
Ratre Sushila, R. Divya, Bhise Rajesh · 2023
Exponential growth of Internet and social media during the past few decades has helped the users to pass any information without even analyzing. Out of these many are fake information which has got no authenticity and relevance. The fake news can be misleading and can pose dangerous threat to public health especially in situations of pandemic like Covid-19. Incorrect or misleading information may include information regarding medical, medicine, doctors, presciprtions etc. Sometimes even an expert has to analyse various aspects simultaneously to classify the news as fake or not. To solve this problem we have proposed a prediction model for fake news detection using machine learning approach. Also a comparative analysis of other Machine Learning algorithms like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest and KNN is performed on the dataset to verify the efficiency of the methodology. Based on the experimental results, it can be inferred that SVM classifier gives better performance as compared to others with an accuracy of 82.85%.