Crowdtesting is increasingly popular among researchers to carry out subjective assessments of different services. Experimenters can easily assess to a huge pool of human subjects through crowdsourcing platforms. The workers are usually anonymous, and they participate in the experiments independently. Therefore, a fundamental problem threatening the integrity of these platforms is to detect various types of cheating from the workers. In this poster, we propose cheat-detection mechanism based on an analysis of the workers' mouse cursor trajectories. It provides a jQuery-based library to record browser events. We compute a set of metrics from the cursor traces to identify cheaters. We deploy our mechanism to the survey pages for our video quality assessment tasks published on Amazon Mechanical Turk. Our results show that cheaters' cursor movement is usually more direct and contains less pauses.
- Cursor submovement
ASJC Scopus subject areas
- Computer Networks and Communications