Detecting Fake News

Subscribe: Apple • Android • Spotify • Stitcher • Google • RSS.In this episode of the Data Exchange I speak with Xinyi Zhou,   a graduate student in Computer and Information Science at Syracuse University.  Xinyi and her advisor (Reza Zafarani) recently wrote a comprehensive survey paper entitled “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities”. They set out to organize the many different methods and perspectives used to detect fake news. Their paper is a great resource for anyone wanting to understand the strengths and limitations of various state-of-the-art techniques, and a feel for where the research community might be headed in the near future.Download the 2020 NLP Survey Report and learn how companies are using and implementing natural language technologies.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

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A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].