  The questions in a crowdsourcing task typically exhibit varying degrees of
difficulty and subjectivity. Their joint effects give rise to the variation in
responses to the same question by different crowd-workers. This variation is
low when the question is easy to answer and objective, and high when it is
difficult and subjective. Unfortunately, current quality control methods for
crowdsourcing consider only the question difficulty to account for the
variation. As a result,these methods cannot distinguish workers personal
preferences for different correct answers of a partially subjective question
from their ability/expertise to avoid objectively wrong answers for that
question. To address this issue, we present a probabilistic model which (i)
explicitly encodes question difficulty as a model parameter and (ii) implicitly
encodes question subjectivity via latent preference factors for crowd-workers.
We show that question subjectivity induces grouping of crowd-workers, revealed
through clustering of their latent preferences. Moreover, we develop a
quantitative measure of the subjectivity of a question. Experiments show that
our model(1) improves the performance of both quality control for crowd-sourced
answers and next answer prediction for crowd-workers,and (2) can potentially
provide coherent rankings of questions in terms of their difficulty and
subjectivity, so that task providers can refine their designs of the
crowdsourcing tasks, e.g. by removing highly subjective questions or
inappropriately difficult questions.
