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Overview of the Workshop
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Abstract
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Our social interactions are often made by
electronic means, and are thus recorded in accessible formats. This opens a
range of possibilities for studying human interactions from data such as
social network sites on the web and cell phone communications. These tasks
usually involve massive amount of data (billions of records) that is often
quite noisy and even corrupted. Social and artificial networks suggest new
challenges in modelling machine learning problems
as there are strong spatial and temporal correlations. In this workshop we
will hear researchers from academia and industry share
their insights about the theory and applications of social analytics. Our
focus will be on graph mining and predictive tools that can be used in this
area. We will also consider different applications from recommender
systems, marketing, search, network optimization and other emerging areas.
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Background and
Objectives
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Social analytics is a new and exciting area
of research, which has begun in earnest in the last decade, mainly due to
the availability of data. Machine Learning is well suited to contribute to this
field, which requires learning from large amounts of data in order to gain
a better understanding, provide better predictions and ultimately change
and adjust the network behavior.
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Impact and
Expected Outcome
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Our goal is to bring people from different
fields, ranging from computer science to marketing, from both
academia and industry, to foster a discussion on the different
approaches people have taken to the study of social phenomena. A
successful workshop will introduce people to the tools and data available
for social research in various communities. A primary goal of the workshop
is to create a set of common theoretical problems to work on and to solicit
data from companies to facilitate common research challenges.
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