Abstract
Online platforms are being used for outspreading malicious talk, which creates an impact on the minds of millions. Many distinct approaches have been brought forward to detect this fake news, but very few have been carried out in the actual world. We address this problem of estimating the rumor authentication in a real-world in less time with significantly high accuracy. We design and implement an approach addressing the above issue. We accessed whether the news is fake or not using various Machine learning techniques. We evaluate this algorithm on a set of data set scrapped from random online sites. The result shows that the performance of this improved algorithm is better than the original classification method. And finally, we consider various sizes of data to view and compare the accuracy. KEYWORDS—Support Vector Machine, Decision Tree, Fake Content, Naive Bayes, Machine Learning Models
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