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arXiv · 1610.08216

Huddler: Convening Stable and Familiar Crowd Teams Despite Unpredictable Availability

Niloufar Salehi·Andrew McCabe·Melissa Valentine·Michael Bernstein
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Abstract

Distributed, parallel crowd workers can accomplish simple tasks through workflows, but teams of collaborating crowd workers are necessary for complex goals. Unfortunately, a fundamental condition for effective teams - familiarity with other members - stands in contrast to crowd work's flexible, on-demand nature. We enable effective crowd teams with Huddler, a system for workers to assemble familiar teams even under unpredictable availability and strict time constraints. Huddler utilizes a dynamic programming algorithm to optimize for highly familiar teammates when individual availability is unknown. We first present a field experiment that demonstrates the value of familiarity for crowd teams: familiar crowd teams doubled the performance of ad-hoc (unfamiliar) teams on a collaborative task. We then report a two-week field deployment wherein Huddler enabled crowd workers to convene highly familiar teams in 18 minutes on average. This research advances the goal of supporting long-term, team-based collaborations without sacrificing the flexibility of crowd work.

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Niloufar Salehi, Andrew McCabe, Melissa Valentine, Michael Bernstein. 2016-10-26. Huddler: Convening Stable and Familiar Crowd Teams Despite Unpredictable Availability. https://arxiv.org/abs/1610.08216

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