The Communication Network Within the Crowd

Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
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引用次数: 93

Abstract

Since its inception, crowdsourcing has been considered a black-box approach to solicit labor from a crowd of workers. Furthermore, the "crowd" has been viewed as a group of independent workers dispersed all over the world. Recent studies based on in-person interviews have opened up the black box and shown that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. Put another way, prior work has shown the existence of edges between workers. We build on and extend this discovery by mapping the entire communication network of workers on Amazon Mechanical Turk, a leading crowdsourcing platform. We execute a task in which over 10,000 workers from across the globe self-report their communication links to other workers, thereby mapping the communication network among workers. Our results suggest that while a large percentage of workers indeed appear to be independent, there is a rich network topology over the rest of the population. That is, there is a substantial communication network within the crowd. We further examine how online forum usage relates to network topology, how workers communicate with each other via this network, how workers' experience levels relate to their network positions, and how U.S. workers differ from international workers in their network characteristics. We conclude by discussing the implications of our findings for requesters, workers, and platform providers like Amazon.
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人群中的通信网络
从一开始,众包就被认为是一种从一群工人中征集劳动力的黑箱方法。此外,“人群”被视为一群分散在世界各地的独立工作者。最近基于面对面访谈的研究打开了黑盒子,表明人群不是独立工作者的集合,而是工人之间相互沟通和协作。换句话说,先前的研究表明,工人之间存在边缘。我们在这一发现的基础上,通过绘制亚马逊土耳其机械(Amazon Mechanical Turk)——一个领先的众包平台——工人的整个通信网络,对这一发现进行了扩展。我们执行了一项任务,来自全球各地的10,000多名工人自我报告他们与其他工人的通信联系,从而绘制了工人之间的通信网络。我们的研究结果表明,虽然很大一部分员工看起来确实是独立的,但在其余人群中存在丰富的网络拓扑结构。也就是说,在人群中有一个实质性的通信网络。我们进一步研究了在线论坛的使用如何与网络拓扑相关,工人如何通过该网络相互交流,工人的经验水平如何与他们的网络位置相关,以及美国工人在网络特征上与国际工人有何不同。最后,我们讨论了我们的发现对请求者、工作人员和平台提供商(如Amazon)的影响。
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