Tweeting the Alarm: Exploring the Efficacy of Twitter as a Serial Transmitter during the COVID-19 Pandemic

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Abstract

In this research I seek to understand the efficacy of Twitter as a platform for serial transmission of a crisis communications. As members of the public adopt social media, these platforms are seen as an increasingly strategic channel through which crisis communications can be transmitted. However, there is insufficient understanding of the extent to which these platforms and their users effectively amplify and spread crisis-relevant information during a crisis. I explore these issues in the context of US state health departments and their efforts to inform the public of developments during the COVID-19 pandemic. To do this, I analyze the profiles of the official Twitter account for all 50 state health departments and seek to classify them according to their social presence on social media. Then, I analyze the COVID-19 tweets these health departments sent during the pandemic to better understand how their social media presence and the nature of their messages (tweets, words, URLs, mentions per 7 day) affect the total number of twitter users exposed to each message. My results suggest that systematic differences do exist in the social media presence for state health departments. These differences, as well as the characteristics of the tweets they sent during the pandemic help explain the total number of people exposed to their messages such that short, frequent messages with URLs from departments with high social presence reaching the largest number of users per week. These findings have implications for those responsible for communicating with the public during a crisis and for researchers seeking a better understanding of the flow of crisis communications on social media.

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APA

London, J. (2021). Tweeting the Alarm: Exploring the Efficacy of Twitter as a Serial Transmitter during the COVID-19 Pandemic. In SIGMIS-CPR 2021 - Proceedings of the 2021 Computers and People Research Conference (pp. 5–12). Association for Computing Machinery, Inc. https://doi.org/10.1145/3458026.3462159

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