Transparent Tagging for Strategic Social Nudges on User-Generated Misinformation

TL;DR

This paper models how social network platforms can use transparent tagging to effectively reduce misinformation spread through social nudges, even when misdetection occurs, by applying a Bayesian persuasion framework.

Contribution

It introduces a Bayesian persuaded branching process model to optimize transparent tagging policies for misinformation control under misdetection conditions.

Findings

Optimal policy is transparent tagging despite misdetection.

Model shows how tagging influences user comments and misinformation circulation.

Policy design reduces misinformation spread through social nudges.

Abstract

Social network platforms (SNP) rely heavily on user-generated content to attract users, yet they have limited control over content provision, which leads to misinformation. As countermeasures, SNPs have implemented policies to notify users by tagging the content and influencing users' responses to the tagged content. The population-level response creates a social nudge to the content provider that encourages it to supply more authentic content. Yet, when designing tags to leverage social nudges, SNP must be cautious about misdetection, which impairs its ability to create social nudges. We establish a Bayesian persuaded branching process to study SNP's tagging policy design under misdetection. Misinformation circulation is modeled by a multi-type branching process, where users are persuaded through tags to give positive/negative comments that influence misinformation spread. When translated into posterior belief space, the SNP's problem is reduced to an equality-constrained optimization, the optimal condition of which is given by the Lagrangian characterization. The key finding is that SNP's optimal policy is transparent tagging, albeit misdetection, which nudges the provider not to generate misinformation.