A Novel Technique to Detect the Fake News by Using the Machine Learning Approaches

Abstract

We live in the age of the internet here; everyone relies on different online resources for information about the world. Social media sites like Facebook, and others are becoming more and more popular, any news can travel among millions of users within a matter of seconds. Many people acknowledge what they have read without verifying its authenticity. The uncontrolled spread of fake news can cause severe social and national damage with catastrophic impacts. Thus, the stretch of far-reaching impact any fake news has is a matter of heavy concern. In this paper, we aim to categorize diverse news (fake or real) pieces known in our data set while implementing concepts of Machine Learning in python programming language. We aim to provide the user with a system that can differentiate any news piece as fake or real. The proliferation of fake news has triggered numerous issues in our society recently. As a result, a lot of scholars have been trying to figure out how to spot fake news. Many fake information detection systems utilize the linguistic component of the news piece. They have, however, had trouble spotting highly vague fake news, which can only be spotted after figuring out the meaning and most recent information related to it. The paper proposes a fake news detection system that uses machine learning to address this issue. The bogus news discovery model is created using steps like data collection, data pre-processing, feature removal, and ultimately classification using various classifiers. This is accomplished by using Python programming language-based Machine Learning Algorithm principles. We employ various Machine Learning classification approaches to produce the most accurate outcomes.