  In today's digital world, interaction with online platforms is ubiquitous,
and thus content moderation is important for protecting users from content that
do not comply with pre-established community guidelines. Having a robust
content moderation system throughout every stage of planning is particularly
important. We study the short-term planning problem of allocating human content
reviewers to different harmful content categories. We use tools from fair
division and study the application of competitive equilibrium and leximin
allocation rules. Furthermore, we incorporate, to the traditional Fisher market
setup, novel aspects that are of practical importance. The first aspect is the
forecasted workload of different content categories. We show how a formulation
that is inspired by the celebrated Eisenberg-Gale program allows us to find an
allocation that not only satisfies the forecasted workload, but also fairly
allocates the remaining reviewing hours among all content categories. The
resulting allocation is also robust as the additional allocation provides a
guardrail in cases where the actual workload deviates from the predicted
workload. The second practical consideration is time dependent allocation that
is motivated by the fact that partners need scheduling guidance for the
reviewers across days to achieve efficiency.
  To address the time component, we introduce new extensions of the various
fair allocation approaches for the single-time period setting, and we show that
many properties extend in essence, albeit with some modifications. Related to
the time component, we additionally investigate how to satisfy markets' desire
for smooth allocation (e.g., partners for content reviewers prefer an
allocation that does not vary much from time to time, to minimize staffing
switch). We demonstrate the performance of our proposed approaches through
real-world data obtained from Meta.
