QUEST: Queue Simulation for Content Moderation at Scale
QUEST: Queue Simulation for Content Moderation at Scale
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QUEST：大规模内容审核的队列模拟
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Julián Mestre
中科院分区：
文献类型：
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作者：
Rahul M. Makhijani;P. Shah;Vashist Avadhanula;C. Gocmen;N. Stier;Julián Mestre
社交媒体平台上的内容审核是一项艰巨的挑战，因为此类系统的规模史无前例，通常每天处理数十亿条帖子。一些最大的平台，如Facebook，将机器学习与数千名评审者对平台内容的手动审查结合在一起。运行一个大规模的人类审查系统提出了有趣和具有挑战性的方法论问题，这些问题可以用运筹学技术来解决。我们利用排队论和模拟的思想，研究了大规模地最优操作这样一个审查系统的问题。
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.