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Quest for the Gold Par: Minimizing the Number of Gold Questions to Distinguish between the Good and the Bad

Published: 15 May 2018 Publication History

Abstract

The benefits of crowdsourcing for data science have furthered its widespread use over the past decade. Yet fraudulent workers undermine the emerging crowdsourcing economy: requestors face the choice of either risking low quality results or having to pay extra money for quality safeguards like e.g., gold questions or majority voting. Obviously, the more safeguards injected into the workload, the lower the risks imposed by fraudulent workers, yet the higher the costs are. So, how many of them are really needed? Is there such a 'one size fits all' number? The aim of this paper is to identify custom-tailored numbers of gold questions per worker for managing the cost/quality balance. Our new method follows real life experiences: the more we know about workers before assigning a task, the clearer our belief or disbelief in this worker's reliability gets. Employing probabilistic models, namely Bayesian belief networks and certainty factor models, our method creates worker profiles reflecting different a-priori belief values, and we prove that the actual number of gold questions per worker can indeed be assessed. Our evaluation on real-world crowdsourcing datasets demonstrates our method's efficiency in saving money while maintaining high quality results. Moreover, our methods performs well despite the quite limited information known about workers in today's platforms.

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Cited By

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  • (2024)PrivRo: A Privacy-Preserving Crowdsourcing Service With Robust Quality AwarenessIEEE Transactions on Services Computing10.1109/TSC.2024.337715817:4(1682-1697)Online publication date: Jul-2024

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    cover image ACM Conferences
    WebSci '18: Proceedings of the 10th ACM Conference on Web Science
    May 2018
    399 pages
    ISBN:9781450355636
    DOI:10.1145/3201064
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 15 May 2018

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    Author Tags

    1. crowdsourcing
    2. gold questions
    3. quality control
    4. worker-awareness

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    WebSci '18
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    WebSci '18: 10th ACM Conference on Web Science
    May 27 - 30, 2018
    Amsterdam, Netherlands

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    WebSci '18 Paper Acceptance Rate 30 of 113 submissions, 27%;
    Overall Acceptance Rate 245 of 933 submissions, 26%

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    • (2024)PrivRo: A Privacy-Preserving Crowdsourcing Service With Robust Quality AwarenessIEEE Transactions on Services Computing10.1109/TSC.2024.337715817:4(1682-1697)Online publication date: Jul-2024

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