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In this paper, we aim to gain a better understanding into how paid microtaskcrowdsourcing could leverage its appeal and scaling power by using contests toboost crowd performance and engagement. We introduce our microtask-basedannotation platform Wordsmith, which features incentives such as points,leaderboards and badges on top of financial remuneration. Our analysis focuseson a particular type of incentive, contests, as a means to apply crowdsourcingin near-real-time scenarios, in which requesters need labels quickly. We modelcrowdsourcing contests as a continuous-time Markov chain with the objective tomaximise the output of the crowd workers, while varying a parameter whichdetermines whether a worker is eligible for a reward based on their presentrank on the leaderboard. We conduct empirical experiments in which crowdworkers recruited from CrowdFlower carry out annotation microtasks on Wordsmith- in our case, to identify named entities in a stream of Twitter posts. In theexperimental conditions, we test different reward spreads and record the totalnumber of annotations received. We compare the results against a controlcondition in which the same annotation task was completed on CrowdFlowerwithout a time or contest constraint. The experiments show that rewarding onlythe best contributors in a live contest could be a viable model to deliverresults faster, though quality might suffer for particular types of annotationtasks. Increasing the reward spread leads to more work being completed,especially by the top contestants. Overall, the experiments shed light onpossible design improvements of paid microtasks platforms to boost taskperformance and speed, and make the overall experience more fair andinteresting for crowd workers.

Beyond monetary incentives: experiments in paid microtask contests modelled as continuous-time markov chains