Scientists' Communication With the Public on Twitter During the COVID-19 Pandemic: Text Mining Analysis of Scientists' Tweets (Preprint)
Authors: Chae Seung Woo , Liu Jieli , Shiroiya Harshit Rakesh , Hara Noriko 
Published: January 2023

BACKGROUND

After the COVID-19 pandemic outbreak, laypeople relied on scientists with COVID-19 expertise to obtain trustworthy information, and scientists employed social media to share their COVID-19 knowledge and combat misinformation regarding virus countermeasure efficacy.

OBJECTIVE

This study analyzes how scientists can better communicate with the public on social media by examining scientists’ tweets during the pandemic.

METHODS

We selected 145 scientists with COVID-19 expertise by searching for authors of COVID-related papers in 3 prominent journals (Nature, Science, and The New England Journal of Medicine) who also used Twitter during the pandemic. The scientists were categorized as “popular” or “ordinary” based on if they had at least 10000 followers. We collected all scientists’ tweets between January 2020 and September 2022 and classified those as “COVID-related” or “non-COVID-related,” depending on the presence of the keywords: “COVID,” “Corona,” or “SARS-CoV-2”. We created word clouds to visualize the most-recurring words within each tweet group. Then, using LIWC (a natural-language processing software), we analyzed 7 linguistic characteristics of each tweet: (1) analytical thinking, (2) authenticity, (3) affiliation, (4) power, (5) big words (7 or more letters), (6) positive emotion, and (7) negative emotion. Finally, we compared these results against data from random user tweets.

RESULTS

The most common word within the COVID-related tweets, excluding the 3 keywords, was “vaccine,” perhaps indicating a desire to quickly develop a COVID-19 vaccine, and issues surrounding the vaccines’ releases. The most recurring word within the non-COVID-related tweets was “thank,” representing scientists’ gratitude for the pandemic-era trend of increased open access within academia, which impacted medical and health research at the time. In the LIWC linguistic analyses, the results displayed significant language-use differences between the popular and ordinary groups. The popular group used authentic words significantly more than the ordinary group in both COVID and non-COVID samples, while using analytical, affiliative, power-related, and big words less. Positive emotion was the only variable with a different pattern; whereas the popular scientists used positive emotional words significantly less in the non-COVID sample, those of the COVID sample used these words more. The difference in negative emotion between the 2 groups was insignificant.

CONCLUSIONS

By observing scientists’ linguistic behavior on Twitter during the pandemic, we found that popular scientists tend to “tell their own stories” colloquially. This is atypical, as scientists traditionally shared knowledge formally and top-down. Some popular scientists’ tweets displayed vulnerability, even disclosing knowledge limitations within their research areas. Additionally, popular scientists presented their tweets positively when compared to ordinary scientists, despite the negative nature of COVID-19. To summarize, this study suggests that scientists can better communicate with laypeople via employing casual, authentic, and positive language on social media, rather than the traditional top-down delivery of scientific results.