Noname manuscript No. (will be inserted by the editor) Characterizing and Automatically Detecting Crowdturfing in Fiverr and Twitter

Abstract As human computation on crowdsourcing systems has become pop-ular and powerful for performing tasks, malicious users have started misusing these systems by posting malicious tasks, propagating manipulated contents, and targeting popular web services such as online social networks and search engines. Recently, these malicious users moved to Fiverr, a fast growing micro-task marketplace, where workers can post crowdturfing tasks (i.e., astroturfing campaigns run by crowd workers) and malicious customers can purchase those tasks for only $5. In this manuscript, we present a comprehensive analysis of crowdturfing in Fiverr and Twitter, and we develop predictive models to de-tect and prevent crowdturfing tasks in Fiverr and malicious crowd workers in Twitter. First, we identify the most popular types of crowdturfing tasks found in Fiverr and conduct case studies for these crowdturfing tasks. Second, we build crowdturfing task detection classifiers to filter these tasks and pre-vent them from becoming active in the marketplace. Our experimental result