Purpose: Twitter is being increasingly used by nursing professionals to share ideas, information, and opinions about the global pandemic yet there continues to be a lack of research on how nurse sentiment is associated with major events happening on the frontline. The purpose of the study was to quantitatively identify sentiments, emotions, and trends in nurses’ tweets and to explore the variations in sentiments and emotions over a period in 2020 with respect to the number of cases and deaths of COVID-19 worldwide.
Design: A cross-sectional data mining study was held from March 3, 2020, through December 3, 2020. The tweets related to COVID-19 were downloaded using the tweet ids available from a public website. Data were processed and filtered by searching for keywords related to nursing in the profile description field using the R software and JMP Pro Version 16 and the sentiment analysis of each tweet was done using AFINN, Bing, and NRC lexicon.
Findings: A total of 13,868 tweets from the Twitter accounts of self-identified nurses were included in the final analysis. The sentiment scores of nurses’ tweets fluctuated over time and some clear patterns emerged related to the number of COVID-19 cases and deaths. Joy decreased and sadness increased over time as the pandemic impacts increased.
Conclusions: Our study shows that Twitter data can be leveraged to study the emotions and sentiments of nurses and the findings suggest that the emotional realm of nurses was affected during the COVID-19 pandemic according to the emotional trends observed in tweets.
Clinical Relevance: The study provides insight into what nurses are feeling and findings from this study highlight the importance of developing and implementing interventions targeted at nurses at the workplace to prevent mental health consequences.