Fake News And Tampered Image Detection In Social Networks Using Machine Learning

Fake News detection is much needed in today's world as it has a large impact in our social as well as personal life but involves some challenges due to the limited resources like datasets, research papers. Nowadays lot of information is being shared over social media and people cannot able to differentiate between which information is fake and which is real. People immediately start sharing the news without confirming its authenticity. This further results in spreading of it. Fake news and rumor are the most popular forms of false and unauthenticated information and should be detected as soon as possible for avoiding their dramatic consequences. In previous research, many fake news detection methods were proposed. In this project the final report is generated by predictions of text classification using SVM, Logistic Regression, Gaussian Naive Bayes and tampered image classification by Error Level Analysis using CNN.