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Transparent Tagging for Strategic Social Nudges on User-Generated Misinformation

Ya-Ting Yang, Tao Li, Quanyan Zhu

TL;DR

This paper addresses misinformation on social networks by modeling tagging as a signaling mechanism in a three-player Bayesian persuasion game under imperfect detection. It combines a Bayesian persuaded branching process with a multi-type branching diffusion to analyze population-level misinformation dynamics, and reduces the equilibrium design problem to equality-constrained convex optimization via Bayesian plausibility. A Lagrangian characterization shows that transparent tagging—revealing perceived authenticity to both providers and users—remains optimal even with misdetection, effectively incentivizing content providers to maximize truthful effort. Numerical studies illustrate how detection accuracy and content-provider costs shape the implementable effort and the evolution of negative versus positive comments, highlighting the practical value of transparent tagging for reducing misinformation while maintaining platform activity.

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.

Transparent Tagging for Strategic Social Nudges on User-Generated Misinformation

TL;DR

This paper addresses misinformation on social networks by modeling tagging as a signaling mechanism in a three-player Bayesian persuasion game under imperfect detection. It combines a Bayesian persuaded branching process with a multi-type branching diffusion to analyze population-level misinformation dynamics, and reduces the equilibrium design problem to equality-constrained convex optimization via Bayesian plausibility. A Lagrangian characterization shows that transparent tagging—revealing perceived authenticity to both providers and users—remains optimal even with misdetection, effectively incentivizing content providers to maximize truthful effort. Numerical studies illustrate how detection accuracy and content-provider costs shape the implementable effort and the evolution of negative versus positive comments, highlighting the practical value of transparent tagging for reducing misinformation while maintaining platform activity.

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.

Paper Structure

This paper contains 23 sections, 10 theorems, 44 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Online Misinformation Circulation: A Bayesian Persuasion Modeling
  4. The Bayesian Persuaded Branching Processes Model
  5. Perfect Bayesian Equilibrium
  6. Binary-State Case
  7. Content Spreading Through Multi-type Branching Process
  8. Multi-type Branching Processes
  9. Stochastic Approximation Analysis
  10. Optimality Conditions under Stable Branching
  11. Finite-State Persuasion Game
  12. Perfect Bayesian Equilibrium Characterization: A Lagrangian Approach
  13. Bayesian Plausibility
  14. Feasible Posterior Beliefs
  15. The Lagrangian Characterization
  16. ...and 8 more sections

Key Result

Proposition 1

Consider $\mathbb{E}[M^2] < \infty$ in the multi-type branching process, the $\{\Bar{Z}_{i}\}, \{\Bar{N}_{i}\}$ sequences converge to $\Bar{Z}^*, \Bar{N}^*$ almost surely, where $\Bar{Z}^* = m-1$ and $\Bar{N}^*=\eta^*(\sigma) \Bar{Z}^*$ with $\eta^*(\sigma) = \frac{\alpha_{pn}(\sigma)}{1-\alpha_{nn}

Figures (3)

  • Figure 1: An illustration of the proposed persuasion model, where the misinformation distribution $\theta(\lambda)$ is affected by the content provider and remains unknown to the user. The SNP's misdetection of the underlying content is modeled by $d$.
  • Figure 2: Simulations of online misinformation circulation under fully informative tagging policy. The shaded region indicates the standard deviation of $\eta^*$, while the line represents the mean of $\eta^*$.
  • Figure 3: Simulations of online misinformation circulation when the SNP adopts a hybrid tagging policy. The line represents the mean of $\eta^*$.

Theorems & Definitions (23)

  • Proposition 1
  • Corollary 1
  • Definition 1: Perfect Bayesian Equilibrium in Matrix
  • Proposition 2: Bayesian Plausibility
  • proof
  • Proposition 3
  • proof
  • Proposition 4: Implementable Effort, Feasible Condition
  • proof
  • Corollary 2
  • ...and 13 more