  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.
