Fake News Detection System Using Natural Language Processing: An Optimized Approach

The aim of this research is to create a fake news detection system using natural language processing with an optimized approach. In this work, we recommend a machine learning-based technique for identifying online fake news. Our framework leverages natural language processing techniques and various machine learning algorithms to examine textual records, metadata, and user engagement patterns to distinguish between genuine and fabricated news articles, the vibrant segment of the system takes keyword/text from user and searches for truth probability of the news source and lastly delivers validity of news inputted by user. Python and its libraries were utilized; Django was utilized for the web based organization of the model which offers client side execution using HTML, CSS and Javascript. The design methodology embraced in the design and development of the suggested system is Rapid Application Development (RAD) model. The project was planned in a manner that prospect modifications can be achieved. The conclusion and result from development of the project include the computerization of the whole system increases efficiency, It offers pleasant graphical user interface that proves to be operable while matched to the current system, It successfully overwhelms the interruption in Fake news detection. The System has suitable room for modification in future when needed and prompts authenticity of News and Information. The system can also be used in various areas like social media websites, news companies, radio/television stations, media houses, government establishments, shopping malls, spars, hotels and other public sectors.