STUDY OF CLASSIFIERS FOR THE IDENTIFICATION OF FAKE NEWS

Abstract

A significant source of local and international news for millions of individuals, online social networking websites are rapidly growing. OSNs, on the contrary side, have two disadvantages. Despite the numerous benefits they provide, such as endless, simple communication and instant access to data and news, they can also come with a number of drawbacks and concerns. The spread of misleading information is one of their toughest obstacles. Fake news detection is a difficult & unsolved issue. Nevertheless, designing a solution is anything but straightforward given the unique characteristics and challenges of identifying bogus news on OSNs. Artificial intelligence (AI) techniques, on the other hand, are still incapable of overcoming this difficult obstacle. Even worse, by creating and disseminating false information, artificial intelligence (AI) systems like deep learning and machine learning are being abused to trick customers. Because the material is meant to closely mirror reality, it can occasionally be difficult to assess its reliability using AI alone and without support from outside sources. As a result, it's still very difficult to recognise bogus news automatically. This research seeks to offer a fundamental review of the methods currently used to recognise false news and prevent it from spreading over OSNs in addition to classifying fake news using several classifiers.