Freelancer Influence Evaluation and Gig Service Quality Prediction in Fiverr

The service technology and crowdsourcing movement have spawned a host of successful efforts that promote the rapid development of the human service ecosystem. In this ecosystem, a large number of globally-distributed freelancers are organized to tackle a range of tasks over the web. These crowdsourcing services provide convenience for civilians with lower price and shorter response time. However, the convenience cannot whitewash many unstable factors that are caused by human involvement, such as undefinable reputation, unstable quality, crowdturfing, and etc. In this paper, we present a comprehensive data-driven investigation of one prominent supply-driven human services marketplace-Fiverr-wherein we analyze freelancers' marketing behaviors and their offering services (called 'gigs'). As part of this investigation, we identify the key features that can be used to evaluate freelancers' influence and develop a GSRC (Gig service property + Seller Impact + Customer Review + Semantic Content) model to predict gig service quality. As far as we know, this is the first attempt that involves the service semantic info in the prediction model and integrates all these four aspect factors.