Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media

2024-02-17

AI Summary

This study investigates the amplifying effect of social media misinformation on infectious disease transmission (e.g., COVID-19) and its public health consequences. We develop the first multi-source coupled model integrating county-level rumor exposure data with a large-scale mobility-informed human contact network, combining multilayer network modeling, geographically weighted rumor inference, agent-based epidemic simulation, and real-world mobile trajectory data. Our approach enables the first computationally tractable, quantitative assessment of misinformation–epidemic co-dynamics and reveals the spatially heterogeneous propagation mechanism of vaccine hesitancy driven by misinformation. Simulation results indicate that, under worst-case scenarios, misinformation could lead to millions of additional infections in the United States. The findings provide empirically grounded, spatially explicit priorities for rumor intervention, advancing the integration of digital epidemiology and evidence-based public health policy.

Technology Category

• Application Domains: Misinformation & Fake News
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Social Networks and Social Media: Computational social science
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation.

Problem

• Fake News
• Social Media
• Public Health

Innovation

• Integrated Model
• Misinformation Spread
• COVID-19 Impact