What can words reveal about the COVID-19 pandemic?

A analysis of over 4 million words spoken by public leaders during the pandemic reveals differences in the way they respond to the unfolding pandemic over time, and harbors lessons for future crises.
Published in Healthcare & Nursing
What can words reveal about the COVID-19 pandemic?

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

In times of crisis, individuals adjust their behaviors to meet the challenges before them. People react to crises in different ways, and communicate about them differently as well. Studies have found that people have a tendency to vary the manner in which they deliver public speeches during periods of crisis.

The COVID-19 pandemic has elicited a range of policy responses, with some leaders favoring lockdowns, while others favoring minimal restrictions. The rhetoric of public officials during the pandemic has varied accordingly, as can be seen by examining the speeches given by governors of each US state.

In our paper, The language of crisis: spatiotemporal effects of COVID-19 pandemic dynamics on health crisis communications by political leaders, we examined the words spoken by governors during their regular informational sessions to constituents on the progress of the pandemic and any precautions they should be taking. We assembled a corpus of transcripts of over 1,500 governor speeches, comprising over four million words. We categorized words into semantic and grammatical categories and analyzed their patterns over time in relation to COVID-19 case rates in each state.

We found a number of strong associations between semantic groups and case rates – some expected and some not. Negation words (like “no” and “not”) had strong positive associations with case rates. Words related to hospitals, grandiose descriptions, and words that generally describe bad occurrences also increased. Words related to issuing strict instructions were used more often as cases went up, consistent with governors’ attempts to increase compliance.

On the other hand, words relating to travel and jobs decreased as cases increased, consistent with fewer people traveling and a temporary shift away from economic concerns as pandemic cases rose. Surprisingly, we found a negative association between cases and words relating to helping others, possibly due to increased urgency of promoting social distancing. There was also a negative relationship between cases and words describing urgent situations, which may be the result of governors’ not wanting to incite more panic.

As the pandemic progressed, we found that not only the words themselves changed, but the tense of the words changed as well. There was a change from future tense verbs to past and present tense verbs as cases increased, with governors focusing on what was being done at the time, rather than what was going to be done in the future. Similarly, there was a shift from nouns to verbs and adverbs, consistent with focus on action, and decreased use of personal pronouns, focusing less on who was doing the actions and more on what was being done.

Finally, the average word length decreased as cases went up, particularly in states that had extremely high case rates. This is consistent with increased stress experience by the governors, with previous studies showing average word length dropping under stressful circumstances.

Among the many takeaways of this study, some of the most important ones focus on how the public can assess leaders during times of crisis. Analyzing a public official’s word choice – as well as the length and structure of those words – may help to shed light on certain factors driving their decision-making process. Additionally, these findings and analytical approaches can serve as a basis for further research on the impact and effectiveness of different public communication approaches during times of crisis. Thus, leaders can better inform their own speechmaking to more effectively communicate with their constituents during times of crisis.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Health Care
Life Sciences > Health Sciences > Health Care
  • npj Digital Medicine npj Digital Medicine

    An online open-access journal dedicated to publishing research in all aspects of digital medicine, including the clinical application and implementation of digital and mobile technologies, virtual healthcare, and novel applications of artificial intelligence and informatics.

Related Collections

With collections, you can get published faster and increase your visibility.

Digital twins for precision health

Publishing Model: Open Access

Deadline: Aug 31, 2024

Natural language processing in Clinical Medicine

This Collection welcomes research on Natural Language Processing innovations to improving medical and population health outcomes, with a particular emphasis on computational linguistics approaches and applications for health and digital medicine.

Publishing Model: Open Access

Deadline: Sep 27, 2024