2017/02/11 by Jie Yang, Yang, Jie, Alessandro Bozzon +1
Computer Science · Decision Sciences · Social Sciences · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Personal Information Management and User Behavior #Privacy, Security, and Data Protection #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1702.03385
openalex publication_date 2017/02/11 · openalex created_date 2017/03/16 · openalex updated_date 2026/07/28
Micro-task crowdsourcing has become a successful mean to obtain high-quality data from a large crowd of diverse people. In this context, trust between all the involved actors (i.e. requesters, workers, and platform owners) is a critical factor for acceptance and long-term success. As actors have no expectation for "real life" meetings, thus trust can only be attributed through computer-mediated trust cues like workers qualifications and requester ratings. Such cues are often the result of technical or social assessments that are performed in isolation, considering only a subset of relevant properties, and with asynchronous and asymmetrical interactions. In this paper, we advocate for a new generation of micro-task crowdsourcing systems that pursue an holistic understanding of trust, by offering an open, transparent, privacy-friendly, and socially-aware view on the all the actors of a micro-task crowdsourcing environment.