Stanford professor Johan Ugander is an expert in making sense of messy data. Lately he’s been working to tell fact from fiction online, as news stories spread on social media.
He comes at the question from a unique angle, using machine learning to study the differing patterns in how both types of information spread (or don’t).
In so doing, Ugander has come to some interesting conclusions and, more important, suggests some novel strategies for preventing the spread of misinformation. False stories, he says, are more “infectious,” with wide-ranging consequences for how they spread. Strategies to slow or restrict this infectiousness range from increasing digital literacy to asking potential sharers to consider the factual accuracy of a story they are about to share.
Ugander has also started to take his research in a new direction, criminal justice, working to make sense of the complex data records that a Stanford team has collected to understand California’s parole system, as he tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman.