Fake News Detection Using Deep Learning

Abstract

Abstract: Social media news may be a double-edged sword. There are a number of benefits to utilizing it: It's simple to use, takes little time, and is user-friendly. It's also simple to share socially significant data with others. On the other hand, a number of social networking sites adapt the news based on personal opinions and interests. This sort of misinformation is spread over social media with the intent of causing harm to a person, organization, or institution. Because of the prevalence of fake news, computer tools are needed to detect it. Fake news detection aims to aid users in spotting various sorts of fake news. We can tell if the news is genuine or created if we have encountered fake or authentic news before. We may use a number of models to understand social media news. This is a donation in two ways. We must first give datasets containing both fake and accurate news and conduct multiple experiments before developing a false news detector. Various machine learning techniques are used to categorize the data. Random Forest, Logistic Regression, Naives Bayes, Gradient Boost and Decision Tree techniques are used and compared. It was found that Gradient Boost has the best accuracy.