Title: Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data,
Abstract: We proposed a probabilistic approach to joint modeling of participants'
reliability and humans' regularity in crowdsourced affective studies.
Reliability measures how likely a subject will respond to a question seriously;
and regularity measures how often a human will agree with other
seriously-entered responses coming from a targeted population.
Crowdsourcing-based studies or experiments, which rely on human self-reported
affect, pose additional challenges as compared with typical crowdsourcing
studies that attempt to acquire concrete non-affective labels of objects. The
reliability of participants has been massively pursued for typical
non-affective crowdsourcing studies, whereas the regularity of humans in an
affective experiment in its own right has not been thoroughly considered. It
has been often observed that different individuals exhibit different feelings
on the same test question, which does not have a sole correct response in the
first place. High reliability of responses from one individual thus cannot
conclusively result in high consensus across individuals. Instead, globally
testing consensus of a population is of interest to investigators. Built upon
the agreement multigraph among tasks and workers, our probabilistic model
differentiates subject regularity from population reliability. We demonstrate
the method's effectiveness for in-depth robust analysis of large-scale
crowdsourced affective data, including emotion and aesthetic assessments
collected by presenting visual stimuli to human subjects.