Crowd-sourcing is increasingly being used for providing answers to online polls and surveys. However, existing systems, while taking care of the mechanics of attracting crowd workers, poll building, and payment, provide little that would help the survey-maker or pollster to obtain statistically significant results devoid of even the obvious selection biases.
This paper proposes InterPoll, a platform for programming of crowd-sourced polls. Polls are expressed as embedded LINQ queries, whose results are provided to the developer. InterPoll supports reasoning about uncertainty, enabling t-tests, etc. on random variables obtained from the crowd. InterPoll performs query optimization, as well as bias correction and power analysis, among other features. Making InterPoll queries part of the surrounding program allows for optimizations that take advantage of the surrounding code context. The goal of InterPoll is to provide a system that can be reliably used for research into marketing, social and political science questions.
This paper highlights some of the existing challenges and how InterPoll is designed to address most of them. We outline some of the optimizations and give numerous motivating examples designed to illustrate our system design. Note that this paper is an outline of our vision—we deliberately focus on examples and motivation and leave a detailed technical treatment for future work.