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QUEST: Queue Simulation for Content Moderation at Scale

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arxiv 2103.16816 v1 pith:JA4JJZF6 submitted 2021-03-31 cs.LG

Rahul Makhijani , Parikshit Shah , Vashist Avadhanula , Caner Gocmen , Nicolás E. Stier-Moses , Julián Mestre This is my paper
classification cs.LG
keywords contentreviewscaleoperatingplatformssimulationsystemaddressed
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scheduling in Queueing Systems with Uncertain and Evolving Holding Costs

    cs.DS 2025-05 conditional novelty 7.0 of 10

    A new index policy, OaRC, for scheduling jobs with Markovian uncertain holding costs achieves asymptotically optimal regret that is independent of the state-space size.

  2. Self-Anchored Attention Model for Sample-Efficient Classification of Prosocial Text Chat

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A self-anchored attention classifier using the whole training set as anchor features achieves 0.836 AUC for prosocial chat in Call of Duty: Modern Warfare II, 7.9% above the best benchmark.

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