Date of Award

8-17-2024

Document Type

Dissertation

Abstract

The onset of the COVID-19 global health crisis coincided with an unprecedented rise in health misinformation and false narratives related to the disease. This simultaneous spread of accurate and inaccurate information, referred to as an infodemic, has had observable impacts on the trajectory of the pandemic and the future of public health. While health misinformation has been a factor in previous outbreaks, the increased prominence of social media as an information platform allowed misinformation to spread more widely and rapidly than ever before. This investigation aims to assess the impacts of COVID-19 misinformation in Alaska using a mixed methods approach. The first study uses a machine learning model to describe themes from popular public-facing Alaska-based Facebook pages in which posts or comments containing misinformation proliferated. The second study aims to evaluate the effectiveness of a brief educational intervention in lowering vaccine hesitancy using an online, randomized controlled trial survey. The final study uses a One Health framework to explore how the circulation of false, incomplete, and excessive information affected professionals responding to the COVID-19 pandemic. Findings from these studies offer insight into infodemic dynamics in Alaska, including trends in online misinformation, the need for highly targeted, coordinated communication strategies, and the challenges posed by misinformation across response sectors. First-hand knowledge of the effects of the infodemic revealed a direct impact on the community, professional practice, mental and physical health. The erosion of trust in science and public health along with the unprecedented politicization experienced during the pandemic not only impaired the immediate response but appears to have lasting repercussions on the field of public health. By leveraging these findings, we can enhance emergency preparedness for future public health threats with an informed, proactive, and nimble response.

Handle

http://hdl.handle.net/11122/15519

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