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Related Experiment Videos

Verification in referral-based crowdsourcing.

Victor Naroditskiy1, Iyad Rahwan, Manuel Cebrian

  • 1Electronics and Computer Science, University of Southampton, Southampton, United Kingdom. vn@ecs.soton.ac.uk

Plos One
|October 17, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a model for referral-based crowdsourcing with verification to address pervasive misreporting. The optimal compensation scheme minimizes information retrieval costs, mirroring a successful real-world challenge strategy.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Information Science
  • Social Computing

Background:

  • Online social networks enable large-scale task completion via referral-based crowdsourcing.
  • Previous research focused on incentives without considering pervasive misreporting, a key challenge in real-world applications like the DARPA Red Balloon Challenge.

Purpose of the Study:

  • To formally model and analyze verification mechanisms in referral-based crowdsourcing.
  • To develop a compensation scheme that minimizes the cost of obtaining accurate information.

Main Methods:

  • Introduction of a novel model incorporating costly agent verification efforts.
  • Analysis of penalties for false reports within the crowdsourcing framework.
  • Theoretical derivation of an optimal compensation scheme.

Main Results:

  • The proposed model is the first to formally integrate verification into referral-based crowdsourcing.
  • Identification of an optimal compensation scheme that minimizes the cost of acquiring correct information.
  • Demonstration that the optimal scheme aligns with strategies used in the DARPA Red Balloon Challenge.

Conclusions:

  • Verification is crucial for mitigating misreporting in crowdsourcing.
  • The derived compensation scheme offers a cost-effective solution for accurate information gathering.
  • The model provides a foundation for future research in secure and efficient crowdsourcing systems.