Truthful Team Formation for Crowdsourcing in Social Networks: (Extended Abstract)

This paper studies complex task crowdsourcing by team formation in social networks (SNs), where the requester wishes to hire a group of socially connected workers that can work together as a team. Previous social team crowdsourcing approaches mainly focus on the algorithmic part for social welfare maximization, however, ignore the strategic behavior of workers. In practical crowdsourcing markets, workers are selfish for maximizing their own profit. Within the traditional researches, these selfish workers can be encouraged to manipulate the crowdsourcing system. This untruthful behavior will discourage other workers from participations and is unprofitable for the requester. Thus, a truthful mechanism, guaranteeing that each worker's profit is optimized by behaving honestly, is essential to the success of a crowdsourcing system. Towards this end, in this paper, we develop two efficient truthful mechanisms for the small-scale and large-scale social team crowdsourcing applications, respectively. The experimental results on a real dataset show that compared to the benchmark optimal mechanism, the proposed mechanisms perform well for various scale applications on social welfare maximization.