Verification in referral-based crowdsourcing
File(s)
Published version
Author(s)
Naroditskiy, V
Rahwan, I
Cebrian, M
Jennings, NR
Type
Journal Article
Abstract
Online social networks offer unprecedented potential for rallying a large number of people to accomplish a given task. Here we focus on information gathering tasks where rare information is sought through “referral-based crowdsourcing”: the information request is propagated recursively through invitations among members of a social network. Whereas previous work analyzed incentives for the referral process in a setting with only correct reports, misreporting is known to be both pervasive in crowdsourcing applications, and difficult/costly to filter out. A motivating example for our work is the DARPA Red Balloon Challenge where the level of misreporting was very high. In order to undertake a formal study of verification, we introduce a model where agents can exert costly effort to perform verification and false reports can be penalized. This is the first model of verification and it provides many directions for future research, which we point out. Our main theoretical result is the compensation scheme that minimizes the cost of retrieving the correct answer. Notably, this optimal compensation scheme coincides with the winning strategy of the Red Balloon Challenge.
Date Issued
2012-10-10
Date Acceptance
2012-08-23
Citation
PLOS One, 2012, 7 (10)
ISSN
1932-6203
Publisher
Public Library of Science
Journal / Book Title
PLOS One
Volume
7
Issue
10
Copyright Statement
© Naroditskiy et al. 2012. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MULTIDISCIPLINARY SCIENCES
SOCIAL NETWORKS
SEARCH
INFORMATION
TIES
Crowdsourcing
Humans
Personnel Selection
Reward
Social Media
Social Networking
General Science & Technology
MD Multidisciplinary
Publication Status
Published
Article Number
e45924