Abstract
When running large human computation tasks in the real-world, honeypots play an important role for assessing the overall quality of the work produced. The generation of such honeypots can be a significant burden on the task owner as they require specific characteristics in their design and implementation and continuous maintenance when operating data pipelines that include a human computation component. In this extended abstract we outline a novel approach for creating honeypots using automatically generated questions from a reference knowledge base with the ability to control such parameters as topic and difficulty.
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