Computer scientists and social scientists both find fake news detection to be a fascinating topic. The spread of fake news on social media in recent years has had a significant impact on society. Numerous users all around the world have access to a vast amount of information from numerous sources. Facebook, WhatsApp, and Twitter are a few of the most well-known social media networks that may quickly give compelling data. It is becoming more and more important and difficult to develop a method that can identify bogus news from these platforms. This research presents a machine learning technique that can evaluate an article's authenticity based on the Uniform Resource Locator (URL) that the user enters on the website's front end. Long Short Term Memory (LSTM), Random Forest (randomtree), Random Forest (decision tree), Decision Tree, and Neural Network are five of the most popular machine learning techniques utilised in the project to provide a response informing the viewer of the news's reliability. The human-curated data from http://opensources.co will serve as the basis for our initial definition of what constitutes a credible source. Over 700 phoney news websites are included on OpenSources.co along with about 20 reliable news websites.
final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science.
Components that we will provide are.
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.