  Online Social Networks (OSNs) provide a platform for users to share their
thoughts and opinions with their community of friends or to the general public.
In order to keep the platform safe for all users, as well as to keep it
compliant with local laws, OSNs typically create a set of community standards
organized into policy groups, and use Machine Learning (ML) models to identify
and remove content that violates any of the policies. However, out of the
billions of content that is uploaded on a daily basis only a small fraction is
so unambiguously violating that it can be removed by the automated models.
Prevalence estimation is the task of estimating the fraction of violating
content in the residual items by sending a small sample of these items to human
labelers to get ground truth labels. This task is exceedingly hard because even
though we can easily get the ML scores or features for all of the billions of
items we can only get ground truth labels on a few thousands of these items due
to practical considerations. Indeed the prevalence can be so low that even
after a judicious choice of items to be labeled there can be many days in which
not even a single item is labeled violating. A pragmatic choice for such low
prevalence, $10^{-4}$ to $10^{-5}$, regimes is to report the upper bound, or
$97.5\%$ confidence interval, prevalence (UBP) that takes the uncertainties of
the sampling and labeling processes into account and gives a smoothed estimate.
In this work we present two novel techniques Bucketed-Beta-Binomial and a
Bucketed-Gaussian Process for this UBP task and demonstrate on real and
simulated data that it has much better coverage than the commonly used
bootstrapping technique.
