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Becoming the Super Turker: Increasing Wages via a Strategy from High Earning Workers

by   Saiph Savage, et al.
West Virginia University

Crowd markets have traditionally limited workers by not providing transparency information concerning which tasks pay fairly or which requesters are unreliable. Researchers believe that a key reason why crowd workers earn low wages is due to this lack of transparency. As a result, tools have been developed to provide more transparency within crowd markets to help workers. However, while most workers use these tools, they still earn less than minimum wage. We argue that the missing element is guidance on how to use transparency information. In this paper, we explore how novice workers can improve their earnings by following the transparency criteria of Super Turkers, i.e., crowd workers who earn higher salaries on Amazon Mechanical Turk (MTurk). We believe that Super Turkers have developed effective processes for using transparency information. Therefore, by having novices follow a Super Turker criteria (one that is simple and popular among Super Turkers), we can help novices increase their wages. For this purpose, we: (i) conducted a survey and data analysis to computationally identify a simple yet common criteria that Super Turkers use for handling transparency tools; (ii) deployed a two-week field experiment with novices who followed this Super Turker criteria to find better work on MTurk. Novices in our study viewed over 25,000 tasks by 1,394 requesters. We found that novices who utilized this Super Turkers' criteria earned better wages than other novices. Our results highlight that tool development to support crowd workers should be paired with educational opportunities that teach workers how to effectively use the tools and their related metrics (e.g., transparency values). We finish with design recommendations for empowering crowd workers to earn higher salaries.


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