Fake News Detection Using Different Machine Learning Algorithms

Due to the sheer simple accessibility and exponential rise of data through social media networks, discriminating between false and genuine information has now become challenging. The simplicity by which information may be distributed has resulted in an exponential increase in the amount of information that can be falsified. Where the transmission of fraudulent information is prevalent, the legitimacy of social media networks is also compromised. As a consequence, automatically verifying the information's source, substance, and publisher in order to categorize it as false or real has become a research challenge. Machine learning has proven useful in identifying information, but it has significant limitations. This research describes a variety of machine learning algorithms for identifying bogus and fabricated news. The key goal of this paper is to demonstrate which machine learning algorithm gives maximum accuracy and it was found that the Decision tree gives an accuracy of 99.97 % among all the other algorithms.