Understanding Malicious Behavior in Crowdsourcing Platforms: The Case of Online Surveys.

Crowdsourcing is increasingly being used as a means to tackle problems requiring human intelligence. With the ever-growing worker base that aims to complete microtasks on crowdsourcing platforms in exchange for financial gains, there is a need for stringent mechanisms to prevent exploitation of deployed tasks. Quality control mechanisms need to accommodate a diverse pool of workers, exhibiting a wide range of behavior. A pivotal step towards fraud-proof task design is understanding the behavioral patterns of microtask workers. In this paper, we analyze the prevalent malicious activity on crowdsourcing platforms and study the behavior exhibited by trustworthy and untrustworthy workers, particularly on crowdsourced surveys. Based on our analysis of the typical malicious activity, we define and identify different types of workers in the crowd, propose a method to measure malicious activity, and finally present guidelines for the efficient design of crowdsourced surveys.

Authors: Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze, Gianluca Demartini

PDF: gadiraju_chi2015Download

Breaking Bad: Understanding Behavior of Crowd Workers in Categorization Microtasks

Crowdsourcing systems are being widely used to overcome several challenges that require human intervention. While there is an increase in the adoption of the crowdsourcing paradigm as a solution, there are no established guidelines or tangible recommendations for task design with respect to key parameters such as task length, monetary incentive and time required for task completion. In this paper, we propose the tuning of these parameters based on our findings from extensive experiments and analysis of categorization tasks. We delve into the behavior of workers that consume categorization tasks to determine measures that can make task design more effective.

Authors: Ujwal Gadiraju, Patrick Siehndel, Besnik Fetahu, Ricardo Kawase

PDF: gadiraju_ht2015Download