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A Persuasive Approach to Combating Misinformation

Safwan Hossain, Andjela Mladenovic, Yiling Chen, Gauthier Gidel

TL;DR

The paper tackles misinformation spread on social platforms by leveraging information design through Bayesian persuasion under imperfect predictions. It formulates a noisy persuasion model where a platform signals predicted post-state $\hat{\theta}$ via a confusion matrix $Q^\Theta$, optimizing the signaling scheme through a linear program and analyzing how classifier quality affects platform utility. The authors establish that, under symmetric $Q^\Theta$, the optimal utility is non-decreasing with respect to the convex hull of the confusion-row set and is Lipschitz continuous in $Q^\Theta$. They also introduce a stateful performative model describing how signaling changes user behavior over time, proving convergence to a stable posterior and characterizing conditions for stability. Experiments on synthetic data demonstrate meaningful reductions in misinformation sharing across single-round and performative settings, highlighting the practical potential of information design as a non-censorship intervention.

Abstract

Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting and discuss the broader scope of using information design to combat misinformation.

A Persuasive Approach to Combating Misinformation

TL;DR

The paper tackles misinformation spread on social platforms by leveraging information design through Bayesian persuasion under imperfect predictions. It formulates a noisy persuasion model where a platform signals predicted post-state via a confusion matrix , optimizing the signaling scheme through a linear program and analyzing how classifier quality affects platform utility. The authors establish that, under symmetric , the optimal utility is non-decreasing with respect to the convex hull of the confusion-row set and is Lipschitz continuous in . They also introduce a stateful performative model describing how signaling changes user behavior over time, proving convergence to a stable posterior and characterizing conditions for stability. Experiments on synthetic data demonstrate meaningful reductions in misinformation sharing across single-round and performative settings, highlighting the practical potential of information design as a non-censorship intervention.

Abstract

Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting and discuss the broader scope of using information design to combat misinformation.
Paper Structure (20 sections, 13 theorems, 9 equations, 3 figures, 1 table)

This paper contains 20 sections, 13 theorems, 9 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Our contributions:
  3. Related Works
  4. Model
  5. States and Predictions:
  6. Actions and Utilities:
  7. Signaling Scheme:
  8. Performative Model:
  9. Preliminaries
  10. Noisy Persuasion is Less Powerful
  11. Simplifying the Signaling Scheme
  12. Geometry of Noisy Persuasion
  13. Example
  14. Optimal Noisy Persuasion
  15. Performative Perspectives
  16. ...and 5 more sections

Key Result

Proposition 1

For instance $\mathcal{I} = (u, w, \mu)$ and joint confusion matrix $Q^\Theta$, let $u_\mathcal{I}^*(Q^\Theta)$ represent the optimal platform utility achievable with an arbitrary number of signals. Then it is also possible for the platform to achieve $u_\mathcal{I}^*(Q^\Theta)$ utility using exactl

Figures (3)

  • Figure 1: Noisy prior and signaling for example instance. Edges represent probability
  • Figure 2: Avg % decrease in misinformation shared due to single application of noisy persuasion.
  • Figure 3: Avg % decrease in misinformation shared between prior and stable point

Theorems & Definitions (30)

  • Definition 1
  • Definition 2
  • Definition 3
  • Proposition 1
  • Definition 4
  • Proposition 2
  • Corollary 1
  • Theorem 1
  • Corollary 2
  • Theorem 2
  • ...and 20 more