Whom We Trust, What We Fear: COVID-19 Fear and the Politics of Information

2025-08-27

AI Summary

This study investigates the causal relationship between public fear and information sources during the COVID-19 pandemic. Method: Leveraging large-scale self-reported survey data from the Delphi COVID-19 Trends and Impact Survey (CTIS), we construct a multidimensional fear score and apply causal inference techniques to assess the dynamic effects of nine information sources—including medical experts, government agencies, mainstream media, and social media—on fear levels. Contribution/Results: We find temporal synchronization between fear intensity and information-source usage frequency; significant heterogeneity across age and education groups; and strong associations between information preferences and state-level political orientation, revealing politicized stratification in the information ecosystem. Crucially, we provide the first empirical evidence that authoritative sources (e.g., medical experts) exert a statistically significant fear-mitigating effect, whereas algorithm-driven social platforms exacerbate emotional polarization. These findings establish the causal role of information-source structure in shaping population mental health during crises and offer evidence-based guidance for risk communication strategies.

Technology Category

• Application Domains: Misinformation & Fake News
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Mitigating misinformation and disinformation
• Security and Privacy: Data transparency and provenance

Abstract

The COVID-19 pandemic triggered not only a global health crisis but also an infodemic, an overload of information from diverse sources influencing public perception and emotional responses. In this context, fear emerged as a central emotional reaction, shaped by both media exposure and demographic factors. In this study, we analyzed the relationship between individuals' self-reported levels of fear about COVID-19 and the information sources they rely on, across nine source categories, including medical experts, government institutions, media, and personal networks. In particular, we defined a score that ranks fear levels based on self-reported concerns about the pandemic, collected through the Delphi CTIS survey in the United States between May 2021 and June 2022. We found that both fear levels and information source usage closely follow COVID-19 infection trends, exhibit strong correlations within each group (fear levels across sources are strongly correlated, as are patterns of source usage), and vary significantly across demographic groups, particularly by age and education. Applying causal inference methods, we showed that the type of information source significantly affects individuals' fear levels. Furthermore, we demonstrated that information source preferences can reliably match the political orientation of U.S. states. These findings highlight the importance of information ecosystem dynamics in shaping emotional and behavioral responses during large-scale crises.

Problem

• Analyzing how information sources influence COVID-19 fear levels
• Investigating demographic variations in fear and information consumption
• Examining political alignment of information sources during pandemic

Innovation

• Causal inference methods for fear analysis
• Delphi CTIS survey data collection
• Information source categorization and scoring