A study published by Science found that false information circulates “farther, faster, deeper, and more broadly than the truth” [1]. Social media supports the spread of fake news which magnifies the difficulty of verifying the large amounts of information. In a highly digitalized world, using manual effort to classify misinformation from information is insufficient. Machine Learning (ML) can handle this ever-growing problem quite effectively as automated networks such as recurrent neural networks (RNN) can be built to meet the demands of ever increasing fake new sources which have consequential social, ethical, and physical global effects [2]. Using ML, we aim to verify the authenticity of a news article is a reliable source of information in a more efficient manner than we, as humans, are capable of on our own. Our Fake News Detector will take articles as input and use their titles and text bodies to determine if that corpora is real or fake news. Our report outlines the procedure for which we were able to solve our problem to a high degree of effectiveness.
Youtube link: https://youtu.be/brPf9fb3Beg