  Moderating content in social media platforms is a formidable challenge due to
the unprecedented scale of such systems, which typically handle billions of
posts per day. Some of the largest platforms such as Facebook blend machine
learning with manual review of platform content by thousands of reviewers.
Operating a large-scale human review system poses interesting and challenging
methodological questions that can be addressed with operations research
techniques. We investigate the problem of optimally operating such a review
system at scale using ideas from queueing theory and simulation.
