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Optimal Verification of Rumors in Networks

Luca Paolo Merlino, Nicole Tabasso

Abstract

We study the diffusion of a true and a false message when agents are biased and able to verify messages. As a recipient of a rumor who verifies it becomes informed of the truth, a higher rumor prevalence can increase the prevalence of the truth. We uncover conditions such that this happens and discuss policy implications. Specifically, a planner aiming to maximize the prevalence of the truth should allow rumors to circulate if: verification overcomes ignorance of messages, transmission of information is relatively low, and the planner's budget to induce verification is neither too low nor too high.

Optimal Verification of Rumors in Networks

Abstract

We study the diffusion of a true and a false message when agents are biased and able to verify messages. As a recipient of a rumor who verifies it becomes informed of the truth, a higher rumor prevalence can increase the prevalence of the truth. We uncover conditions such that this happens and discuss policy implications. Specifically, a planner aiming to maximize the prevalence of the truth should allow rumors to circulate if: verification overcomes ignorance of messages, transmission of information is relatively low, and the planner's budget to induce verification is neither too low nor too high.
Paper Structure (11 sections, 4 theorems, 21 equations, 2 figures)

This paper contains 11 sections, 4 theorems, 21 equations, 2 figures.

Table of Contents

  1. Introduction
  2. The Model
  3. Diffusion of Truth and Rumor
  4. Optimal Verification
  5. Discussion
  6. Individual Verification Choices
  7. Agents (Sometimes) Negatively React to Messages
  8. Negative vs. Positive Effects of the Rumor
  9. Targeted Verification
  10. Conclusions
  11. Proofs

Key Result

Proposition 1

Suppose that there is some verification, i.e., $\alpha>0$. Then,

Figures (2)

  • Figure 1: A summary of the potential opinions an agent $i$ may hold, depending on her type, the message received by agent $j$, and verification.
  • Figure 2: Steady state prevalence of the truth, $\theta_0$, as a function of $\alpha$, for $\lambda=2$ and $x=0.3$.

Theorems & Definitions (4)

  • Proposition 1
  • Proposition 2
  • Proposition 3
  • Proposition 4