Crisis communication on social media - lessons from Covid-19

ABSTRACT

Social media networks are increasingly important tools for crisis communication and the Novel Coronavirus 2019 (COVID-19) provides a unique opportunity to explore the role of social media in crisis communication. The goal of this research is to better understand how from political leaders adapt their communication strategy to on-the-ground changes during a crisis and how the communication of crisis-relevant information on social media affects the public’s perception of the response effort. We achieve this through an analysis of pandemic data from The Covid Tracking Project at The Atlantic, survey data from the Covid States Project, and pandemic-relevant Twitter statuses posted by US state governors. Our results show that governors alter their communication patterns and tone after developments in the pandemic and that such changes in communication patterns are accompanied by subsequent changes in public perception of the governor’s response to the pandemic. Implications for theory and practice are discussed.

KEYWORDS:

• COVID-19
• big data
• social media
• text analytics
• exploratory
• crisis communication
• tone

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Notes

1. Across The Country, Governors Are Taking The Lead On Coronavirus Response. (17 April 2020). NPR.Org. https://www.npr.org/2020/04/17/837511580/across-the-country-governors-are-taking-the-lead-on-coronavirus-response

2. The official CDC Twitter account first announced the organisation’s monitoring of nCoV-2019 on January 15th – https://twitter.com/CDCgov/status/1217517963278012416

3. The COVID Tracking Project at The Atlantic. (2020). The COVID Tracking Project. https://covidtracking.com/about

4. THE STATE OF THE NATION: A 50-STATE COVID-19 SURVEY. (2020). The COVID States Project. https://covidstates.org/about-us

5. Hospital admissions are reported by 40 of the 50 states. The results for admits were produced with a reduced sample (N = 1480). All other variables were calculated with the full sample (N = 1850).