Bridging online and offline dynamics of the face mask infodemic.

Background: Online infodemics have represented a major obstacle to the offline success of public health interventions during the COVID-19 pandemic. Offline contexts have likewise fueled public susceptibility to online infodemics. We combine a large-scale dataset of Twitter conversations about face masks with high-performance machine learning tools to detect low-credibility information, bot activity, and stance toward face masks in online conversations. We match these digital analytics with offline data regarding mask-wearing and COVID-19 cases to investigate the bidirectional online-offline dynamics of the face mask infodemic in the United States. Results: Online prevalence of anti-mask over pro-mask stance predicts decreased offline mask-wearing behavior and subsequently increased COVID-19 infections. These effects are partially influenced by low-credibility information and automated bot activity, which consistently feature greater anti-mask stance online. Despite their purported controversy, mask mandates generally decrease anti-mask stance online and increase mask-wearing offline, thus reducing future COVID-19 infections. Notable asymmetries are observed, however, between states run by Democratic and Republican governors: the latter tend to see higher levels of low-credibility information and anti-mask stance online, and thus lower mask-wearing and higher infection rates offline. Conclusions: These findings contribute new insights around collective vulnerabilities to online infodemics and their links to evolving offline crises. We highlight the need to synergize and sustain targeted online campaigns from legitimate information sources alongside offline interventions in and beyond the pandemic. [ABSTRACT FROM AUTHOR]