Network Prebunking Problem: Optimizing Prebunking Targets to Suppress the Spread of Misinformation in Social Networks

2025-08-01

AI Summary

This paper addresses the problem of target selection for prebunking interventions in social networks to suppress misinformation diffusion at minimal cost. We formulate it as a network-level combinatorial optimization problem and prove its NP-hardness. To model directional influence propagation, we propose a localized directed influence model based on Maximum Influence Arborescence (MIA), and design a robust approximation algorithm that efficiently identifies near-optimal intervention nodes under parameter uncertainty. The method balances fidelity in influence simulation with computational scalability. Experiments on real-world social network datasets demonstrate that our approach significantly reduces the scale of misinformation spread, maintaining high performance and stability under both full observability and parameter uncertainty. Our core contributions are: (i) the first network optimization framework for prebunking, (ii) theoretical guarantees on approximation quality, and (iii) a practical, robust algorithm for scalable deployment.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Combinatorial Optimization
• Application Domains: Misinformation & Fake News

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Mitigating misinformation and disinformation
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

As a countermeasure against misinformation that undermines the healthy use of social media, a preventive intervention known as prebunking has recently attracted attention in the field of psychology. Prebunking aims to strengthen individuals' cognitive resistance to misinformation by presenting weakened doses of misinformation or by teaching common manipulation techniques before they encounter actual misinformation. Despite the growing body of evidence supporting its effectiveness in reducing susceptibility to misinformation at the individual level, an important open question remains: how best to identify the optimal targets for prebunking interventions to mitigate the spread of misinformation in a social network. To address this issue, we formulate a combinatorial optimization problem, called the network prebunking problem, to identify optimal prebunking targets for minimizing the spread of misinformation in a social network. We prove that this problem is NP-hard and propose an approximation algorithm, MIA-NPP, based on the Maximum Influence Arborescence (MIA) approach, which restricts influence propagation around each node to a local directed tree rooted at that node. Through numerical experiments using real-world social network datasets, we demonstrate that MIA-NPP effectively suppresses the spread of misinformation under both fully observed and uncertain model parameter settings.

Problem

• Identify optimal prebunking targets in social networks
• Minimize misinformation spread via combinatorial optimization
• Propose MIA-NPP algorithm for effective intervention

Innovation

• Combinatorial optimization for prebunking targets
• MIA-NPP algorithm based on local trees
• Effective under observed and uncertain parameters