Structure of Crowdsourcing Community Networks

Abstract

Due to the interest of organizations and academics, crowdsourcing is emerging as an area of targeted social networking. The recent popularity and notable rise of crowdsourcing provides us with the opportunity to study these emerging communities to standardize and facilitate the crowdsourcing process for future development of such platforms. In this paper, we conduct a large and comprehensive study of the structure of a number of crowdsourcing communities (CCs). We study various properties of association (ASSO) and interaction (INTR) networks in an attempt to compare them with existing networks, such as online social networks (OSNs) and the World Wide Web (WWW) network. We obtained data for five successful CCs with nearly two million vertices and nearly six million edges, as well as data for four popular social network sites, Flickr, YouTube, Orkut, and LiveJournal, with more than 11 million vertices and over 328 million edges. We also obtained WWW data containing over 18 million vertices and over 64 million edges. We believe this is the first structural comparative study of CC networks with social and WWW networks at this scale. Our study reveals that CC networks—both ASSO and INTR—are smaller and less symmetrical than OSNs. Similar to OSNs and WWW, degree distributions of CC networks follow power-law distribution. CCs and WWW do not suffer influence dilution as is the case in OSNs. Different than OSNs, members of CC networks tend to connect to others with varying degrees, as is the case with WWW.