Identifying Fake News using Machine Learning

Abstract

Fake data is purposely or accidentally transmitted throughout the internet. It has long been a social issue, and in the digital age, the average person now has easy access to all of the information available online. This is affecting a growing population of people who are technologically blind. One of the most serious problems in the modern day is fake news, which has the capacity to affect people's minds and influence their judgments. On web browsers, there are a few plugins that provide real-time information about the veracity of news. The algorithms used to create these plugins have a significant impact on them. The goal is to create a project that will propose which of the three implemented algorithms is the best for further development by the developer. Machine learning classification methods such as SVM, naive bayes, logistic regression, decision tree, and random forest are taught to detect if news is fake or real, and then compared based on metrics.