Document Type

Article

Publication Date

2026

DOI

10.3390/app16104740

Publication Title

Applied Sciences

Volume

16

Issue

10

Pages

4740

Abstract

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news regions, to be detected and targeted where strategic shifts are most likely to occur. Importantly, fact-checking is modeled as an external intervention that may induce a probabilistic lasting correction on agents’ latent opinions after removal, capturing more realistic post-intervention behavior. Unlike static strategies that assume fixed fact-checker distributions, the proposed approach continuously reallocates interventions toward structurally critical regions, while adaptively adjusting resource intensity based on misinformation prevalence. Extensive simulations on small-world, scale-free, and random networks show that the adaptive model consistently outperforms static baselines, reducing the final fake-news prevalence by over 90%, accelerating suppression, and improving overall system efficiency. Statistical tests confirm the significance of these improvements (𝑝< 0.001), while sensitivity analyses demonstrate robustness across parameter settings and intervention assumptions.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. 

Data Availability

Article states: "The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author."

Original Publication Citation

Firouzjaee, M. T., Naderi, G., Gore, R., & Moghim, N. (2026). Adaptive boundary-aware fact-checker placement for misinformation suppression in social networks. Applied Sciences, 16(10), Article 4740. https://doi.org/10.3390/app16104740

ORCID

0000-0003-4065-6146 (Gore)

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