Panos Ipeirotis

Title: Targeted Crowdsourcing with a Billion (Potential) Users
Speaker: Panos Ipeirotis, Professor in Department of Information, Operations,
and Management Sciences at NYU Stern School of Business
Date: April 10, 2018
Time: 12:00-1:00pm
Room: Gates-Hillman Complex 6501


Abstract:
We describe Quizz, a gamified crowdsourcing system that simultaneously
assesses the knowledge of users and acquires new knowledge from them.
Quizz operates by asking users to complete short quizzes on specific
topics; as a user answers the quiz questions, Quizz estimates the
user’s competence. To acquire new knowledge, Quizz also incorporates
questions for which we do not have a known answer; the answers given
by competent users provide useful signals for selecting the correct
answers for these questions. Quizz actively tries to identify
knowledgeable users on the Internet by running advertising campaigns,
effectively leveraging “for free” the targeting capabilities of
existing, publicly available, ad placement services. Quizz quantifies
the contributions of the users using information theory and sends
feedback to the advertising system about each user. The feedback
allows the ad targeting mechanism to further optimize ad placement.
Our experiments, which involve over ten thousand users, confirm that
we can crowdsource knowledge curation for niche and specialized
topics, as the advertising network can automatically identify users
with the desired expertise and interest in the given topic. We present
controlled experiments that examine the effect of various incentive
mechanisms, highlighting the need for having short-term rewards as
goals, which incentivize the users to contribute. Finally, our
cost-quality analysis indicates that the cost of our approach is below
that of hiring workers through paid-crowdsourcing platforms, while
offering the additional advantage of giving access to billions of
potential users all over the planet, and being able to reach users
with specialized expertise that is not typically available through
existing labor marketplaces.


Bio:
Panos Ipeirotis is a Professor and George A. Kellner Faculty Fellow at
the Department of Information, Operations, and Management Sciences at
Leonard N. Stern School of Business of New York University. He
received his Ph.D. degree in Computer Science from Columbia University
in 2004. He has received nine “Best Paper” awards and nominations and
is the recipient of the 2015 Lagrange Prize, for his contributions in the
field of social media, user-generated content, and crowdsourcing.


Find out more about Panos Ipeirotis at http://www.ipeirotis.com/.