Teenu Xavier, Student, University of Cincinnati
Increasingly, Twitter is being used by nursing professionals to share ideas, information, and opinions. Many nurses have also used this platform to raise concerns about the effects of workplace conditions on their mental health. While these tweets are a good data source for research, often serving as an adjunct to established research approaches, there is a lack of research on nurses’ tweets, since it is difficult to filter them on the Twitter API. This posters demonstrates how JMP can be used to filter tweets from nurses from a large data set.
A cross‐sectional data mining study was conducted from March 3-Dec. 3, 2020. Nearly 355,000,000 tweets related to COVID‐19 were downloaded using the tweet IDs available from a public website. Using Text Explorer in JMP Pro 16, data were processed and filtered by searching for keywords related to nursing in the profile description field.
After Text Explorer filtered the key terms and phrases related to the nursing profession, 13,868 tweets were extracted from 5,476 unique Twitter user IDs of self‐identified nurses. Once the tweets had been collected, they became a huge data source (comprised of thousands of users) that facilitated robust analyses to explore nurses' opinions and concerns on various topics related to promoting health and reducing risk .
