Fake News Detection Using Machine Learning Methods

The paper is about the detection of unauthenticated news using Machine-learning methods with different algorithms. There is lot of scope to check the reality of the news received from various sources like websites, blogs, e-content. To identify the fake news, there is a need of some application in real time. Many methods were proposed earlier to observe fake news such as style-based, propagation-based and user-based. Automatic fake news detection application can be generated using natural language processing, information retrieval techniques, as well as graph theory. Language modeling is used to predict the missing or next word in a sentence based on the context. It is believed that mainstream media platforms are publishing fake news to grasp the attention of readers; most likely, it is done to increase the number of visitors on that particular page so that with an increasing number of visitors the page could claim more advertisement. This paper proposes an efficient method to detect fake news with better accuracy by using the available data set to detect the news is FAKE or REAL. Various methods are used for collecting the data and the data mining techniques are applied to clean and visualize it. Data mining helps to differentiate between the qualities of data depending upon its properties. The performance of detecting news only from the body of news is not sufficient but also social engagements should be considered. The objective of the work is to provide end-users with a robust solution so that they can figure out phishy and misguiding information. This technique combines the title and the body of the news to predict fake news more efficiently. The application is concerned with finding a result that could be used to identify fake news to help users.