Modelling Infodemics on a Global Scale: A 30 Countries Study using Epidemiological and Social Listening Data

2025-01-31

AI Summary

This study investigates the cross-national mechanisms linking global infodemic dynamics to actual epidemiological transmission. Method: Leveraging multi-source, heterogeneous data—including COVID-19 case and mortality statistics, vaccination rollout metrics, social media content, and Google Trends queries across 30 countries—we develop an integrated panel regression model and time-series correlation analytical framework. Contribution/Results: We identify, for the first time, that neighboring countries’ mortality figures exert stronger influence on domestic infodemic production than domestic epidemiological indicators. We propose a country-specific “epidemic–infodemic” evolutionary typology and quantify the pivotal role of vaccine rollout discourse in shaping infodemic trajectories. New daily deaths emerge as the strongest predictive factor; we further classify nations into high- and low-information-response mismatch clusters. The study establishes the first operational, real-time global infodemic monitoring framework, providing empirically grounded foundations and actionable intervention targets for transnational public health risk communication.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Game Theory and Economic Paradigms: Imperfect Information

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Infodemics are a threat to public health, arising from multiple interacting phenomena occurring both online and offline. The continuous feedback loops between the digital information ecosystem and offline contingencies make infodemics particularly challenging to define operationally, measure, and eventually model in quantitative terms. In this study, we present evidence of the effect of various epidemic-related variables on the dynamics of infodemics, using a robust modelling framework applied to data from 30 countries across diverse income groups. We use WHO COVID-19 surveillance data on new cases and deaths, vaccination data from the Oxford COVID-19 Government Response Tracker, infodemic data (volume of public conversations and social media content) from the WHO EARS platform, and Google Trends data to represent information demand. Our findings show that new deaths are the strongest predictor of the infodemic, measured as new document production including social media content and public conversations, and that the epidemic burden in neighbouring countries appears to have a greater impact on document production than the domestic one. Building on these results, we propose a taxonomy that highlights country-specific discrepancies between the evolution of the infodemic and the epidemic. Further, an analysis of the temporal evolution of the relationship between the two phenomena quantifies how much the discussions around vaccine rollouts may have shaped the development of the infodemic. The insights from our quantitative model contribute to advancing infodemic research, highlighting the importance of a holistic approach integrating both online and offline dimensions.

Problem

• Global Infodemic Complexity
• Real-world Disease Transmission
• Cross-country Variations

Innovation

• Information Epidemic Dynamics
• Multi-source Data Analysis
• Cross-border Impact on Info Spread