A Task Decomposition Framework for Surveying the Crowd Contextual Insights
Polls, as the most common method of eliciting crowd insights are getting more and more popular. From predicting election results, to customer satisfaction, to scientific surveys, polls play a crucial rule in revealing the true sentiment of a community. However, when participation in a poll is subject to having some specific expertise and skills, finding sufficient number of participants for a given poll is a serious challenge. In this paper, we propose a new method of polling crowd contextual insight which is based on decomposing a poll into some sub-polls and recruiting participants to answer the given questions. We also take into account the probability of engagement of a participant in a poll to make sure that we recruit sufficient number of suitable workers. The proposed method is implemented and tested using the simulated data, build based on a public data dump from Stack overflow. The evaluation results show the superiority of our proposed method over the other related work.