  Content moderation (removing or limiting the distribution of posts based on
their contents) is one tool social networks use to fight problems such as
harassment and disinformation. Manually screening all content is usually
impractical given the scale of social media data, and the need for nuanced
human interpretations makes fully automated approaches infeasible. We consider
content moderation from the perspective of technology-assisted review (TAR): a
human-in-the-loop active learning approach developed for high recall retrieval
problems in civil litigation and other fields. We show how TAR workflows, and a
TAR cost model, can be adapted to the content moderation problem. We then
demonstrate on two publicly available content moderation data sets that a TAR
workflow can reduce moderation costs by 20% to 55% across a variety of
conditions.
