(untitled) (International Journal of Innovative Research in Computer Science & Technology, 2021)
Due to the extreme growing use of social media and online news media, there has been a rise in fake news recently. It has become much easier to spread fake news than it was before. This type of fake news, if widely circulated, could have a significant impact. As a result, it is necessary to take steps to reduce or distinguish between true and false news. We design a system to verifying such type of news and extract correct news or provide correct news corresponding to the fake news. On a text-based dataset, we give an overview of false news detection using various classifiers such as Passive Aggressive Classifier, Random forest, Logistic regression and decision tree classifier gets better results, as seen by the work done. Also top ten recommendations corresponding to the real news is displayed through our proposed model.