Abstract

This thesis adopts a business analytics approach by merging data-driven and theory-driven approaches to demonstrate new opportunities for addressing public health issues such as vaccine hesitancy and health crisis management. It also examines the threat of selective sharing on social media exacerbating polarization and vaccine hesitancy.
The first essay tackles vaccine hesitancy by quantitatively analyzing eight years of data from the U.S. Vaccine Adverse Event Reporting System. This study addresses vaccine safety concerns by highlighting the non-severity of reported adverse events. Cognitive biases have significant roles in connecting such concerns to vaccine hesitancy. This essay identifies and categorizes fifteen cognitive biases influencing individuals’ vaccine decision-making. These findings underscore the importance of utilizing public data sources to mitigate vaccine safety concerns and inform public health communications strategies to counteract cognitive biases influencing vaccine hesitancy.
In two studies, the second essay delves into the role of social media in managing health crises, proposing the Social Media Health Crisis Management (SMHCM) model. By analyzing Canadian authorities’ use of Twitter (now X) during the COVID-19 pandemic for 13 months, study 1 demonstrates how authorities can facilitate shared situational awareness among citizens by disseminating declarative, procedural, and strategic knowledge. In Study 2, the SMHCM model proposes that authorities must publish posts containing declarative, procedural, and strategic knowledge to satisfy citizens’ informational needs and propagate emotional support content to cover their emotional needs during crises, increasing user engagement. The SMHCM model fills a knowledge gap by explaining how authorities can use social media to manage health crises, offering insights into effective health crisis communication strategies.
The third essay explores confirmation bias-induced selective sharing among social media users engaged in vaccine discourse, revealing how selective sharing contributes to polarization. This essay identifies pronounced confirmation bias by analyzing over 6.6 million posts from prominent vaccine discourse participants on Twitter. It also examines the interaction dynamics between vaccine stance groups (anti-vaccine, pro-vaccine, and libertarian). Its findings advance our understanding of confirmation bias-induced selective sharing in the context of vaccine hesitancy and highlight the complex challenges of countering polarized opinions and fostering constructive dialogue on social media.