Statistics were reported for the Tweets such as type (original vs quote), number of tweets per week, reach and engagement through number of followers, like, and retweets, and metrics such as retweets and mentions. The Linguistic Inquiry and Word Count (LIWC) program was used to analyze emotional tone and temporal focus of tweets. For emotional tone, the "positive emotion" and "negative emotion" categories were used, with subcategories such as "Anger," "sadness," and "anxiety." For temporal focus, categories of "past focus," "present focus," and "future focus" were used. Groups were not mutually exclusive. 

Emergency Medicine Influencers’ Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis Quantitative Analysis

Tweets were analyzed inductively by a team of four. They first ran through a sample of 50 tweets to code initial categories. Then they refined these categories iteratively. To strengthen the validity of their analysis, they did member checking, memoing, reflexivity, and triangulation of data.

Emergency Medicine Influencers’ Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis Qualitative Analysis

This was a mixed methods paper, using both quantitative and qualitative analysis.

University of California, Berkeley

This is a mixed-methods study that looked at the messages of emergency medical (EM) Twitter users during the COVID-19 pandemic, characterizing themes, tones, viewpoints, and engagement. 

Emergency Medicine Influencers’ Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis

Leibowitz, M. K., Scudder, M. R., McCabe, M., Chan, J. L., Klein, M. R., Trueger, N. S., & McCarthy, D. M. (2021). Emergency Medicine Influencers’ Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis. Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health. https://escholarship.org/uc/item/5bq5n5tf 

They obtained a list of EM influencers from two published network analyses, and used a sample of 50 that meet the criteria of being on one of the lists, living in the United States, and having recognition within the EM Twitter community.

The George Washington University Libraries Tweetsets data platform was used to access the "Coronavirus Tweet IDs" dataset. From this dataset, tweets from their EM sample were extracted from the dates of March 3, 2020 to May 1, 2020. Only original tweets and quote tweets were extracted, not retweets and replies. They obtained a sample of 1315 tweets. 

Emergency Medicine Influencers’ Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis Data Collection

- Tweets were split almost evenly between original tweets and quote tweets
- median of 16 tweets per user
- engagement: median of 25 likes per tweet and 7 retweets per tweet
- most tweets were either positive (34.6%) or neutral (31%)
- 15% of tweets were negative
- most tweets focused on the present (76.7%)
- 34.4% of tweets shared links to news outlets talking about testing, cases, or other local stories
- 28.9% had information about patient care, protocols, or linked to research publications
- 18.1% provided advice or resources related to COVID-19
- 16.8% contained personal stories related to the pandemic
- 12.3% were political or related to government action

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