Fake news has been a problem since the internet boom. Websites that keep us up to date with what's going on in the world are the perfect breeding ground for bad news and fake news. Fighting fake news is important because the world is knowledge-based. People do not make important decisions based on information; they also form their own ideas. Incorrect information can cause serious damage. It is not possible to identify all messages from a contact. This article attempts to speed up the fake news detection process by recommending a reliable fake news classification method. Machine learning contains different algorithms like naive Bayes, passive-aggressive classifiers and deep neural networks used eight different datasets from different sources. The text also includes the analysis and results of each model. With the right standards and the right tools, the task of detecting fake news will not be trivial.

Natural language processing, Machine learning; Logistic regression, Support vector machine, Fake new detection, TF-IDF.