Abstract: With the popularity of mobile smart terminals,crowdsourcing to collect large-scale perceptual data becomes easier and easier.The selfishness of crowdworkers makes them want to get the most pay with the least effort,and even collude with each other and submit crowdsourced data arbitrarily,resulting in poor quality of crowdsourced task completion.This paper proposes a jury-based quality control strategy,a mechanism that solves the data validation problem.To address the behaviors that degrade the quality of crowdsourcing,this paper uses the proposed community influence detection algorithm(CIDA) to detect conspiracy leaders and their organizations after determining the presence of spam employees and conspiracy organizations,and finally uses an improved similarity detection algorithm(PI-Cosine) to screen out for spam employees.These two aspects are used to improve the quality of crowdsourcing data.Experiments show that the proposed method improves the accuracy of 12.3% over Cosine similarity detection algorithm in accuracy and F1-score measures.

Key words: Crowdsourcing, Quality control, CIDA algorithm, PI-Cosine similarity detection, Spam