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.

翻译：社交媒体平台内容的调节是一个巨大的挑战,因为这类系统的规模空前庞大,通常每天处理数十亿个职位。 一些最大的平台,如Facebook混合机学习,由数千名审查者手工审查平台内容。 大规模的人文审查系统带来了有趣的、具有挑战性的方法问题,可以通过操作研究技术加以解决。 我们调查了利用排队理论和模拟的理念优化规模操作这种审查系统的问题。