Dealing with the COVID-19 crisis: Theoretical application of social media analytics in government crisis management

Little theory-grounded research addresses how to use social media strategically in government public relations through machine learning. To fill this gap, we propose a way to optimize social media analytics to manage issues and crises by using the framework of attribution theory to analyze 360,861 tweets. In particular, we examined the attribution of crisis responsibility related to the spread of COVID-19 and its relations to the negative emotions of U.S. citizens on Twitter for six months (from January 20 to June 30, 2020). The results of this study showed that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis for the Trump administration. In addition, the federal government’s lack of response and inability to handle the outbreak led to citizens’ engagement and amplification of negative tweets that blamed the Trump White House. Theoretical and practical implications of the results are discussed.