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Verification in Referral-Based Crowdsourcing

Author

Listed:
  • Victor Naroditskiy
  • Iyad Rahwan
  • Manuel Cebrian
  • Nicholas R Jennings

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.

Suggested Citation

  • Victor Naroditskiy & Iyad Rahwan & Manuel Cebrian & Nicholas R Jennings, 2012. "Verification in Referral-Based Crowdsourcing," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-7, October.
    • Handle: RePEc:plo:pone00:0045924
    DOI: 10.1371/journal.pone.0045924
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    References listed on IDEAS

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    1. Eric S. Maskin, 2008. "Mechanism Design: How to Implement Social Goals," American Economic Review, American Economic Association, vol. 98(3), pages 567-576, June.
    2. Jon M. Kleinberg, 2000. "Navigation in a small world," Nature, Nature, vol. 406(6798), pages 845-845, August.
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    Cited by:

    1. Marta Poblet & Esteban García-Cuesta & Pompeu Casanovas, 0. "Crowdsourcing roles, methods and tools for data-intensive disaster management," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    2. Naroditskiy, Victor & Stein, Sebastian & Tonin, Mirco & Tran-Thanh, Long & Vlassopoulos, Michael & Jennings, Nicholas R., 2014. "Referral Incentives in Crowdfunding," IZA Discussion Papers 7995, Institute of Labor Economics (IZA).

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