Machine Learning for Fake News Detection Analysis

Abstract

The COVID-19 outbreak has required some health and financial decisions to be made in an unwieldy manner. This has spread uncertainty and lies all over the world. The transmission of false information has been compounded by the problems with fake news. Many of them gave up on newspapers, magazines, and other print media in favor of Internet pleasure. Online entertainment has become the primary news source for a sizable percentage of the population due to its ease of access, low cost, and rapid spread. In some circumstances, bogus information spreads faster than true information to gain popularity over internet entertainment and divert people from the underlying issues. People spread false information using online entertainment for commercial and political benefit. To avoid a harmful influence on society, it is critical to immediately recognize bogus information in all systems. To demonstrate the efficiency of the grouping on the dataset, we produced and tested numerous AI computations independently for this assignment, which looks into research on the recognition of fake news. The Jupyter Notebook stage of this project was used, and the execution was assessed.