Broad stakeholder participation in regulatory policymaking via online commenting platforms has become the norm in many advanced democracies around the world. In recent years, a policy debate has emerged over the dangers posed to the process by fake comments that impersonate ordinary citizens. This paper helps to clarify the terms of this debate by evaluating a contentious and prominent case in the United States, the nearly 24 million comments from the Federal Communications Commission's 2017 Restoring Internet Freedom proceeding. Using a two-step methodology that combines a computationally efficient search algorithm and a neural network language model, I show that regulators were able to cite much of the relevant information submitted in public comments by relying on longstanding methods of information gathering through interest groups, despite the fact that fake comments outnumbered others 3 to 1. The results suggest that fake comments did not impede regulators' ability to extract specialized information from the public consultation process, but may have distorted signals from mass comment campaigns about constituent mobilization.

中文翻译：

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虚假在线评论是否对监管决策构成威胁？来自美国互联网监管的证据

利益相关者通过在线评论平台广泛参与监管政策制定已成为全球许多发达民主国家的常态。近年来，关于冒充普通公民的虚假评论给这一过程带来的危险，出现了一场政策辩论。本文通过评估美国一个有争议的突出案例，即联邦通信委员会 2017 年恢复互联网自由程序中的近 2400 万条评论，帮助澄清这场辩论的条款。使用结合计算效率高的搜索算法和神经网络语言模型的两步法，我表明监管机构能够依靠长期以来通过利益集团收集信息的方法来引用公众评论中提交的大部分相关信息，