In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This project addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This project introduces a novel automatic fake news credibility inference model, namely FAKE DETECTOR. This project reviews various fake news detection methods involving feature extraction methods like Count vectorizer, TF-IDF Vectorizer, Word Embedding and also, different classification algorithms like SVM, Logistic Regression and Gradient Boosting, Random Forest, Decision Trees, KNN and XG-Boost.