EAGER: Opinion Spam in Digital Rulemaking: Techniques, Effects, and Interventions

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

One of the pillars of representative government in the United States is people's ability to participate in setting rules and regulations. Regulatory agencies are required (with some exceptions) to solicit comments from various publics (e.g., general citizenry, affected organizations, interest groups) to learn about the potential consequences of proposed rule changes. To extend participation and reduce costs, the commenting process has been digitized and often takes place through the internet. However, digitization opened the door to opinion spam (e.g., mass, computer-generated, or fraudulent comments) that may undermine the rulemaking process by deceiving agency evaluators and manipulating what citizens' actual attitudes are regarding proposed regulations. Opinion spam complicates the evaluation that agencies must perform in setting rules and threatens the legitimacy of the rulemaking process in the eyes of stakeholders. This project investigates ways in which opinion spam might be prevented and provides evidence regarding which techniques are most effective, thereby preserving (or potentially restoring) public trust in digital rulemaking. In three phases, this project examines threats to digital rulemaking and tests mitigation approaches to reduce opinion spam. Phase 1 includes a series of interviews with submitters of comments, comment evaluators, and scholarly experts on mis/disinformation to gauge how these groups conceive of opinion spam and its prevalence in commenting discourse and to uncover potential interventions that may limit the submission of opinion spam or help agencies detect its submission. Phase 2 includes generating machine learning datasets (e.g., legitimate, fictitious, and automated text replacement or text recombination comments) and models for distinguishing fictitious/artificial comments from legitimate comments. Phase 3 integrates the findings from the prior phases and includes selecting several viable opinion-spam mitigating strategies and testing their efficacy in randomized, controlled experiments. This multi-method, interdisciplinary investigation contributes to the theory of coordinated influence campaigns. The project develops a syntax-aware deep learning model for detecting fictitious comments and helps determine which mitigation approaches work better for reducing opinion spam. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.