Mining Online Data Across Social Networks

Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow.

Learn more: http://scpd.stanford.edu/

Introduction.
Meet The Speaker.
Many data is a Network!.
Networks: Rich Social Data.
Networks: Size *matters.
Networks: Structure & Process.
Why study Web and networks?.
Projects: Link Prediction.
Projects: Friends vs. Foes.
Projects: Predicting Enemies.
Theory of Structural Balance.
Theory of Status.
Friends vs. Foes, Trust vs. Distrust.
Online (social) media.
Tracking Information on the Web.
How is news being made?.
Modeling influence.
Influence curves of media types.
Inferring the Diffusion Network.
Inferring networks.
Maximizing the Influence.
Influential blogs & Information outbreaks.
Reflections.
Directions.
References.
Autumn Quarter 2011-12.
Graduate Portfolio.
Course Enrollment.
Please indicate your level of interest in the Mining Massive Datasets graduate certificate.
Stanford Center for Professional Development.