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 present tutorial investigates techniques for social media modeling,
analytics and optimization. First we present methods for collecting
large scale social media data and then discuss techniques for
coping with and correcting for the effects arising from missing and
incomplete data. We proceed by discussing methods for extracting
and tracking information as it spreads among the users. Then
we 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. As
the information often spreads through implicit social and information
networks we present methods for inferring networks of influence
and diffusion. Last, we discuss methods for tracking the flow
of sentiment through networks and emergence of polarization.