Detecting Fake News: An Investigation into The Effectiveness of Machine Learning Algorithms

Abstract

The problem of fake news and misinformation has become increasingly prevalent in today’s society, leading to a growing need for effective fact-checking and fake news detection tools. These tools aim to identify false or misleading information in digital media and help users make informed decisions. Fact-checking involves verifying the accuracy of information and sources, while fake news detection employs machine learning algorithms to analyze text, images, and multimedia content for evidence of deception. The results of these analyses can be used to classify news items as true, false, or uncertain, allowing users to understand better the credibility of the information they are consuming. Fact-checking and fake news detection are crucial for maintaining a well-informed and trustworthy media landscape, and ongoing research is necessary to improve these accuracy and reliability, too.