Date: Fri, 17 Aug 2018 15:06:04 +0000
<p>The scale and frequency with which information can be distributed on social media makes the problem of fake news a rapidly metastasizing issue. To do any content filtering or labeling demands an algorithmic solution.</p> <p>In today's episode, Kyle interviews Kai Shu and Mike Tamir about their independent work exploring the use of machine learning to detect fake news.</p> <p>Kai Shu and his co-authors published <a href="https://arxiv.org/abs/1708.01967">Fake News Detection on Social Media: A Data Mining Perspective</a>, a research paper which both surveys the existing literature and organizes the structure of the problem in a robust way.</p> <p>Mike Tamir led the development of <a href="https://www.fakerfact.org/login">fakerfact.org</a>, a website and Chrome/Firefox plugin which leverages machine learning to try and predict the category of a previously unseen web page, with categories like opinion, wiki, and fake news.</p>
