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. 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. In this talk, I will describe our attempt to quantify this discovery by mapping the entire communication network of workers on Amazon Mechanical Turk, a leading crowdsourcing platform. We executed a task in which over 10,000 workers from across the globe self-reported 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 examined 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. These findings have implications for requesters, workers, and platform providers. This talk is based on joint work with Ming Yin, Mary Gray, and Sid Suri.