News Authenticator

Description

In 2016, a Pew Research Center poll showed that 64% of U.S. adults experienced confusing regarding basic facts due to fake news. Similarly, a 2018 MIT study showed that false news is proliferated more quickly than real information on social media platforms. Despite the wealth of information accessible on the internet, it continually becomes more difficult to sort through news articles and distinguish true information from propaganda, advertising, or other misleading articles. Using artificial intelligence classification and natural language processing (NLP) techniques, an algorithm was created to automatically detect and identify false news. The system utilizes several NLP algorithms to analyze sentence structure, diction, and tone of an article. These algorithms successfully identify the discrepancies in language and syntax which are often present in a false article. Using the NLP analysis of an article, a trained neural-net classifies the article based on its characteristics as real or fake. The study successfully created a platform to efficiently and rapidly detect fake news allowing media consumers to easily distinguish between basic facts and fake news.