PhD Candidate, College of Nursing, University of Cincinnati, Cincinnati, Ohio, USA.
Assistant Professor, Biostatistician, College of Nursing, University of Cincinnati, Cincinnati, Ohio, USA.
J Nurs Scholarsh. 2022 Sep;54(5):613-622. doi: 10.1111/jnu.12775. Epub 2022 Mar 27.
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.
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.
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.
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.
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.
护理专业人员越来越多地使用 Twitter 分享有关全球大流行的想法、信息和意见，但仍缺乏研究护士情绪如何与前线发生的重大事件相关联。本研究的目的是定量识别护士在 Twitter 上发布的情绪、情感和趋势，并探讨 2020 年期间随着全球 COVID-19 病例和死亡人数的变化，情绪和情感的变化。
这是一项横断面数据挖掘研究，于 2020 年 3 月 3 日至 12 月 3 日进行。使用公共网站提供的 tweet ID 下载与 COVID-19 相关的推文。使用 R 软件和 JMP Pro Version 16 通过在配置文件描述字段中搜索与护理相关的关键字来处理和筛选数据，并使用 AFINN、Bing 和 NRC 词典对每条推文进行情感分析。
最终分析包括来自自我认定的护士的 Twitter 账户的 13868 条推文。随着大流行影响的增加，护士推文的情绪评分随时间波动，一些明显的模式与 COVID-19 病例和死亡人数有关。随着时间的推移，快乐减少，悲伤增加。
我们的研究表明，Twitter 数据可用于研究护士的情绪和情感，研究结果表明，根据推文观察到的情绪趋势，COVID-19 大流行期间护士的情感领域受到影响。
该研究深入了解了护士的感受，研究结果强调了针对工作场所护士制定和实施干预措施以预防心理健康后果的重要性。
