  Online crowdsourcing provides a scalable and inexpensive means to collect
knowledge (e.g. labels) about various types of data items (e.g. text, audio,
video). However, it is also known to result in large variance in the quality of
recorded responses which often cannot be directly used for training machine
learning systems. To resolve this issue, a lot of work has been conducted to
control the response quality such that low-quality responses cannot adversely
affect the performance of the machine learning systems. Such work is referred
to as the quality control for crowdsourcing. Past quality control research can
be divided into two major branches: quality control mechanism design and
statistical models. The first branch focuses on designing measures, thresholds,
interfaces and workflows for payment, gamification, question assignment and
other mechanisms that influence workers' behaviour. The second branch focuses
on developing statistical models to perform effective aggregation of responses
to infer correct responses. The two branches are connected as statistical
models (i) provide parameter estimates to support the measure and threshold
calculation, and (ii) encode modelling assumptions used to derive (theoretical)
performance guarantees for the mechanisms. There are surveys regarding each
branch but they lack technical details about the other branch. Our survey is
the first to bridge the two branches by providing technical details on how they
work together under frameworks that systematically unify crowdsourcing aspects
modelled by both of them to determine the response quality. We are also the
first to provide taxonomies of quality control papers based on the proposed
frameworks. Finally, we specify the current limitations and the corresponding
future directions for the quality control research.
