A context-aware approach for trustworthy worker selection in social crowd

摘要: Crowdsourcing applications like Amazon Mechanical Turk (AMT) make it possible to address many difficult tasks (e.g., image tagging and sentiment analysis) on the internet full use of wisdom crowd, where worker quality is one most crucial issues for task owners. Thus, a challenging problem how effectively efficiently select high workers, so that online can be accomplished successfully under certain budget. The existing methods crowd selection mainly based measurement those who have register crowdsourcing platforms. With connect OSNs applications, social contexts relationships trust between participants positions assist requestors or group trustworthy workers. In this paper, we first present contextual network structure concept Strong Social Component (SSC), which emblems workers values. Then, propose novel index SSC, new efficient effective algorithm C-AWSA find complete with quality. results our experiments conducted four real OSN datasets illustrate superiority method in selection.