DataHack Radio #21: Detecting Fake News using Machine Learning with Mike Tamir, Ph.D.
Introduction
Fake news is one of the biggest scourges in our digitally connected world. That is no exaggeration. It is no longer limited to little squabbles – fake news spreads like wildfire and is impacting millions of people every day.
How do you deal with such a sensitive issue? Millions of articles are being churned out every day on the internet – how do you tell real from fake? It’s not as easy as turning to a simple fact checker. They are typically built on a story-by-story basis. Can we turn to machine learning?
It’s a prevalent and pressing issue – and hence we invited Mike Tamir, Ph.D., as our guest on DataHack Radio. Mike has been working on a project called FakerFact that aims to identify and separate truth from fiction. His team’s approach is based on using machine learning algorithms of the Natural Language Processing (NLP) variety.
In this episode, Kunal and Mike discuss several aspects of the FakerFact algorithms, including:
- The idea behind FakerFact
- How Mike