Tutorial - Part 1: Models of Information Flow and Social Media

Wednesday, March 28, 2012 - 10:00am - 10:45am
Keller 3-180
Jure Leskovec (Stanford University)
Online social media represent a fundamental shift of how information
is being produced, transferred and consumed. User generated content in
the form of blog posts, comments, and tweets establishes a connection
between the producers and the consumers of information. Tracking the
pulse of the social media outlets, enables companies to gain feedback
and insight in how to improve and market products better. For
consumers, the abundance of information and opinions from diverse
sources helps them tap into the wisdom of crowds, to aid in making
more informed decisions.

The talk investigates machine learning techniques for modeling social
networks and social media. First part will discuss methods for
extracting and tracking information as it spreads among users. We will
examine methods for extracting temporal patterns by which information
popularity grows and fades over time. We show how to quantify and
maximize the influence of media outlets on the popularity and
attention given to particular piece of content, and how to build
predictive models of information diffusion and adoption. Second part
will focus on models for extracting structure from social networks and
predicting emergence of new links in social networks. In particular,
we will examine methods based on Supervised Random Walks for learning
to rank nodes on a graph and consequently recommend new friendships in
social networks. We will also consider the problem of detecting dense
overlapping clusters in networks and present efficient model based
methods for network community detection.
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