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The abundance of data made it easy to collect and analyze
data from online user behavior and discussions. Although it is
encouraging to see businesses using such data and making data-driven
decisions, we often see decisions based on flawed analyses, or simply
using data that measure the wrong things. Panos will illustrate cases,
where simple reading of available data points into one conclusion,
while a deeper study uncovers a different truth. Examples will be drawn from online reputation systems, online reviews, crowdsourcing, and
other case studies.
Panos Ipeirotis is an Associate Professor at the Department of
Information, Operations, and Management Sciences at Leonard N. Stern
School of Business of New York University. His recent research
interests focus on crowdsourcing and on mining user-generated content
on the Internet. He has received three “Best Paper” awards (IEEE ICDE
2005, ACM SIGMOD 2006, WWW 2011), two “Best Paper Runner Up” awards
(JCDL 2002, ACM KDD 2008), and is also a recipient of a CAREER award
from the National Science Foundation. He also blogs about
crowdsourcing and Mechanical Turk on his blog A Computer Scientist in
a Business School, an
activity that seems to generate more interest and recognition than any
of the above.