23/08/2020

Prediction of hourly earnings and completion time on a crowdsourcing platform

Anna Lioznova, Alexey Drutsa, Vladimir Kukushkin, Anastasia Bezzubtseva

Keywords: hourly earnings, task completion time, crowdsourcing marketplace

Abstract Paper Similar Papers

Abstract: We study the problem of predicting future hourly earnings and task completion time for a crowdsourcing platform user who sees the list of available tasks and wants to select one of them to execute. Namely, for each task shown in the list, one needs to have an estimated value of the user’s performance (i.e., hourly earnings and completion time) that will be if she selects this task. We address this problem on real crowd tasks completed on one of the global crowdsourcing marketplaces by (1) conducting a survey and an A/B test on real users; the results confirm the dominance of monetary incentives and importance of knowledge on hourly earnings for users; (2) an in-depth analysis of user behavior that shows that the prediction problem is challenging: (a) users and projects are highly heterogeneous, (b) there exists the so-called "learning effect" of a user selected a new task; and (3) the solution to the problem of predicting user performance that demonstrates improvement of prediction quality by up to 25

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