QUEST: Queue Simulation for Content Moderation at Scale

Rahul Makhijani, Parikshit Shah, Vashist Avadhanula, Caner Gocmen, Nicolás E. Stier-Moses, Julián Mestre

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.

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