Wear a Mask, Save a Life? Insights From COVID-19 on Shifting Digital Influence Amid High Epistemic Uncertainty During Crisis

Abstract

This study examines opinion leadership and information diffusion on Twitter (X) during crisis periods with high epistemic uncertainty among the public. By analysing a tweet dataset captured during a unique COVID-19 period marked by conflicting face mask guidelines, we explore how crisis situations like this reshape social media influence dynamics. Our findings reveal complex and paradoxical relationships between traditional indicators of opinion leadership and actual influence, mediated through engagement metrics. Content relevance and engagement patterns can outweigh traditional user attributes in determining influence, with non-traditional opinion leaders emerging through creative, highly engaging crisis-related posts. A network structure of community clusters with limited cross-community flow further defines influence pathways. Our study highlights the importance of flexible, distributed models of online crisis information diffusion that consider the dynamic interplay between user attributes, network topology, engagement patterns and contextual factors. These insights also suggest potential benefits in exploring more adaptive, context-sensitive approaches to managing information flow in digital environments during crises.