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

TL;DR

This paper formulates the network prebunking problem as an NP-hard combinatorial optimization task, proposing an efficient greedy algorithm to select targets that effectively reduce misinformation spread in social networks.

Contribution

It introduces the network prebunking problem, proves its computational hardness, and develops a scalable approximation algorithm based on influence maximization techniques.

Findings

MIA-NPP algorithm effectively suppresses misinformation spread.

The problem is NP-hard with a submodular objective.

Numerical experiments validate the algorithm's efficiency and effectiveness.

Abstract

As a countermeasure against misinformation that undermines the healthy use of social media, a preventive intervention known as \textit{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 \textit{network prebunking problem}, which aims to select optimal prebunking targets that minimizes the spread of misinformation in a social network under limited intervention budgets. We show that the problem is NP-hard and that its objective function is monotone and submodular, which provides a theoretical foundation for approximation guarantees of greedy algorithms. However, since the greedy algorithm is computationally expensive and does not scale to large networks, we propose an efficient 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.