Meme-tracking, Scheduling, and the Flow of On-Line Information
Jon Kleinberg


The flow of information through on-line networks has created a complex landscape of media sources and led to rich datasets that provide glimpses into how news and other forms of real-time Web information are produced, shaped, and consumed.  We begin by discussing methods for studying how news stories spread through such a system, using an approach that tracks short pieces of as they travel and mutate across sources.  This type of analysis can be effective at capturing temporal patterns over a daily time-scale --- in particular, the succession of story lines that evolve, compete for attention, and collectively produce an effect that commentators refer to as the `news cycle.' We then show how a detailed analysis of temporal dynamics can suggest novel optimization problems in the scheduling of Web information. Specifically, given a supply of featured content and data on user attention over time, we consider how to sequence the content in a way that maximizes the size of the audience.


Jon Kleinberg is on the faculty of the Computer Science Department at Cornell University, where he holds the position of Tisch University Professor. His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, and the recipient of MacArthur, Packard, and Sloan Foundation Fellowships, the Nevanlinna Prize, the ACM-Infosys Foundation Award, and the National Academy of Sciences Award for Initiatives in Research.

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