Fake News Detection using Machine Learning Algorithm

In our modern society where internet is ubiquitous, everyone relies on various online resources for news. Along with the rise in use of social media platforms like Facebook, Twitter etc. news spread rapidly among various users within a really short span of your time. The spread of fake news has far reaching consequences like creation of biased opinions to swaying election outcomes for the benefit of certain candidates. Moreover, spammers use appealing news headlines to get revenue using advertisements via click-baits. During this project, we aim to perform a binary classification of varied news articles available online with the assistance of concepts per Decision tree algorithm and Naive Bayes Classification. Fake data detection is that the most vital problem to be addressed within recent years, there's lot of research happening during this field due to its serious impacts on the readers, researchers, government and personal agencies working together to resolve the problem. This project represents a hybrid approach for fake data detection using the multinomial voting algorithm. The list of algorithms that are used here is Decision Tree and Naïve Bayes algorithms. All these two algorithms use training data because the bag of words model which was created using Count vectorizer. Experimental data has collected from the kaggle data world. Python is used as a language to verify and validate the result.