A Theoretical Model of False Information Control

TL;DR

This paper presents a theoretical model extending the SI framework to analyze false information spread, providing decision boundaries for effective interventions and highlighting the importance of early action to reduce costs.

Contribution

It introduces a novel analytical model for false information control that offers decision boundaries and insights for policymakers, extending traditional epidemic models.

Findings

Early intervention reduces overall costs

Analytical decision boundaries guide effective control strategies

Model provides insights despite complex intermediate functions

Abstract

When considering a specific event, news that accurately reflects the ground truth is deemed as real information, while news that deviates from the ground truth is classified as false information. False information often spreads fast due to its novel and attention-grabbing content, which poses a threat to our society. By extending the Susceptible-Infected (SI) model, our research offers analytical decision boundaries that enable effective interventions to get desirable results, even when intermediate functions cannot be analytically solved. These analytical results may provide valuable insights for policymakers in false information control. When assessing intervention costs using the model, the results indicate that the sooner we intervene, the lower the overall intervention cost tends to be.