Leveraging human factors to enhance query answering in crowdsourcing systems

In recent years, crowdsourcing has become essential in a wide range of Web applications. Human factors play a key role in achieving high quality answers in crowdsourcing-based solving tasks. The most major factor is pertained to the uncertainty of workers about the responses that they provide to resolve the task at hand. On the other hand, workers may have diverse levels of expertise and skill. It is then important to take into account both the degrees of uncertainty and expertise when aggregating the set of worker answers. In this paper, we investigate some advanced crowdsourcing aggregation methods to find the correct answers by leveraging both expertise and uncertainty of workers in a unified way. Workers' uncertainty is represented in a possibilistic way, while a fine-grained scale for interpreting the degrees of skill is introduced. Finally, we present some comprehensive experiments to validate the effectiveness of our proposal.