The Influence of Fake News on Social Media: Analysis and Verification of Web Content during the COVID-19 Pandemic by Advanced Machine Learning Methods and Natural Language Processing

Abstract

The purpose of this research was to analyze the prevalence of fake news on social networks, and implicitly, the economic crisis generated by the COVID-19 pandemic, as well as the identification of solutions for filtering and detecting fake news. In this context, we created a series of functions to identify fake content, using information collected from different articles, through advanced machine learning methods with which we could upload and analyze the obtained data. The methodology proposed in this research determined a higher accuracy of fake news collected from Facebook, one of the most powerful social networks for the dissemination of informative content. Thus, the use of advanced machine learning methods and natural language processing code led to an improvement in the detection of fake news compared to conventional methods.