Leveraging Natural Language Processing and Machine Learning for Efficient Fake News Detection

Abstract

In today's digital world, the internet deeply ingrains in all facets of our lives. People highly depend on myriad of online sources for news. The proliferation of fake news is increasingly recognized as a significant peril to democracy, journalism, and the principles of freedom of expression. As the use of social media platforms such as Facebook, Twitter, and others accelerates, any news can promptly circulate among thousands of users within a remarkably short interval of time. Additionally, spammers use enticing news headlines as click-baits to generate profits, using advertisements. To address fake news detection, certain endeavors have been put to develop tools, which prove to be helpful. Yet, it remains crucial to handle the more challenging scenarios, where trustworthy sources heedlessly propagate fake news. This calls for the development of a comprehensive end-to-end solution. The objective of this research is to perform binary classification, using Natural Language Processing and Machine Learning concepts on different news articles to categorize them into fake and real. The findings of this research demonstrate the impact of machine learning techniques in performing this task efficiently.