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How Predictable is the Spread of Information?

About the series

Jake Hofman is a Senior Researcher at Microsoft Research in New York City, where he works in the field of computational social science. Prior to joining Microsoft, he was a Research Scientist in the Microeconomics and Social Systems group at Yahoo! Research. He is an Adjunct Assistant Professor of Applied Mathematics and Computer Science at Columbia University and runs Microsoft's Data Science Summer School to promote diversity in computer science. He holds a B.S. in Electrical Engineering from Boston University and a Ph.D. in Physics from Columbia University.

How does information spread in online social networks, and how predictable are online information diffusion events?  Despite a great deal of existing research on modeling information diffusion and predicting "success" of content in social systems, these questions have remained largely unanswered for a variety of reasons, ranging from the inability to observe most word-of-mouth communication to difficulties in precisely and consistently formalizing different notions of success.
This talk will attempt to shed light on these questions through an empirical analysis of billions of diffusion events under one simple but unified framework.  We will show that even though information diffusion patters exhibit stable regularities in the aggregate, it remains surprisingly difficult to predict the success of any particular individual or single piece of content in an online social network.  Evidence from our simulations further suggests that, rather than resulting from any shortcomings in our estimates or models, this unpredictability may be a hallmark of the information diffusion process itself.

To view the webinar, please register here:  http://www.tvworldwide.com/events/nsf/170719/

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