The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing

摘要: As human computation on crowdsourcing systems has become popular and powerful for performing tasks, malicious users have started misusing these by posting propagating manipulated contents, targeting web services such as online social networks search engines. Recently, moved to Fiverr, a fast-growing micro-task marketplace, where workers can post crowdturfing tasks (i.e., astroturfing campaigns run crowd workers) customers purchase those only $5. In this paper, we present comprehensive analysis of Fiverr. First, identify the most types found in marketplace conduct case studies tasks. Then, build task detection classifiers filter prevent them from becoming active marketplace. Our experimental results show that proposed classification approach effectively detects achieving 97.35% accuracy. Finally, analyze real world impact purchasing Fiverr quantifying their target site. part analysis, current security inadequately detect crowdsourced manipulation, which confirms necessity our approach.