Abstract

AbstractBroad 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.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call