How Predictable is Information Diffusion?

Monday, March 28, 2016 - 5:30pm to 6:30pm
Davis Auditorium, 412 CEPSR, Schapiro Center
530 West 120th Street
New York, NY 10027
United States

I3 Data Science Innovation Seminar

Jake Hofman, Senior Researcher
Microsoft Research

How Predictable is Information Diffusion?

How does information spread in online social networks and how predictable are online diffusion events? Despite a great deal of existing research on modeling information diffusion and predicting success 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 clarify these issues through an empirical analysis of billions of diffusion events under one simple but unified framework. We will show that despite stable regularities in aggregate diffusion patterns, it remains surprisingly difficult to predict the success of any particular individual or piece of content in an online social network, with our best performing models explaining only half of the empirical variance in outcomes. We conclude by exploring this limit theoretically through a series of simulations that suggest that it is the diffusion process itself, rather than our ability to estimate or model it, that is responsible for this unpredictability.

Speaker(s): 

Jake Hofman

Jake Hofman

Jake Hofman is a Senior Researcher at Microsoft Research in New York City, where his work in computational social science involves applications of statistics and machine learning to large-scale social data. Prior to joining Microsoft, he was a member of the Microeconomics and Social Systems group at Yahoo! Research. Jake is also an Adjunct Assistant Professor of Applied Mathematics at Columbia University, where he has designed and taught classes on a number of topics ranging from biological physics to applied machine learning. He holds a B.S.

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