2014/06/03 by Kyumin Lee, Lee, Kyumin, Steve Webb +3 · 2 citations
Computer Science · Physics and Astronomy · #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Internet Traffic Analysis and Secure E-voting #Mobile Crowdsensing and Crowdsourcing #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Spam and Phishing Detection #cs.CY #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1406.0574
arxiv created 2014/06/03 · openalex publication_date 2014/06/03 · arxiv updated 2014/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As human computation on crowdsourcing systems has become popular 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 paper, we present a comprehensive analysis of Fiverr. First, we identify the most popular types of crowdturfing tasks found in this marketplace and conduct case studies for these crowdturfing tasks. Then, we build crowdturfing task detection classifiers to filter these tasks and prevent them from becoming active in the marketplace. Our experimental results show that the proposed classification approach effectively detects crowdturfing tasks, achieving 97.35% accuracy. Finally, we analyze the real world impact of crowdturfing tasks by purchasing active Fiverr tasks and quantifying their impact on a target site. As part of this analysis, we show that current security systems inadequately detect crowdsourced manipulation, which confirms the necessity of our proposed crowdturfing task detection approach.