III: Small: Collaborative Research: Mining Information Propagation on the Web

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Real-time information is a fundamental emerging issue in the creationand management of Web content. Increasingly, rather than consultingrelatively static sources that are indexed on a periodic basis,people refer to information on news sites, blogs, social-networkingsites, and Twitter feeds that change dynamically and spread rapidly.This project will study how information content varies over time, howit is transmitted through underlying social networks, and how itsrecipients assemble it into larger units. The project will explorenew techniques for addressing these issues, based on novel methodsfor tracking, analyzing, and presenting information that evolves andspreads rapidly over time. The resulting approach aims to transformimportant aspects of the ways in which real-time information on theWeb is handled.First the fundamental units of information that spread through theunderlying information networks will be identified. From a set ofnearly 1 billion news media articles and blog posts (approx. 6TB ofdata), and a collection of 500 million tweets from Twitter, small(generally textual) units of information will be identified thatremain relatively stable as they spread through the Web. Thetemporal variation within these basic units will be analyzed andmodeled. This modeling will include connections with biologicalmodels of epidemics, as well as new frameworks that exploit thefundamental differences between biological and social contagion.Finally, the temporal variation will be related to network-levelmodels for the diffusion of this information. Generally, the actualnetworks on which real-time information spreads cannot be directlyobserved, nor can the influence of any particular node in the networkbe directly measured. Therefore, the project will develop machine-learning techniques that infer these hidden networks and unobservedlevels of influence.For more information see the project web site at:http://snap.stanford.edu/proj/mipro