HealthCare Fake News Detection: A New Approach Using Feature Selection Concept

Life is now lot easier than it was before the development of Internet-based technologies. Social media platforms have become exponentially more popular, which has improved human communication as well as the ability to link individuals in distant locations. However, social networking sites have also occasionally been used for immoral and illegal actions. People’s mental and physical health has gotten worse as a result of bogus news being spread on social media during the COVID-19 epidemic. Thus, a number of research have been conducted to automatically detect the false information about COVID-19 using a variety of intelligent algorithms in order to control the flow of false information about the novel coronavirus. However, the performance of the forecasting models has varied according to investigations. In this research, we offer an intelligent model for the automatic identification of fake news about healthcare utilizing machine learning and deep learning principles. Simulation and evaluation results on real-life healthcare datasets reveal the efficiency of the suggested model in differentiating between fake news and real news. It achieves an improvement of 6%, for accuracy contrasted with traditional systems.