Alpha Epsilon, PhD Candidate and Clinical Instructor, Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
Assistant Professor, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
J Nurs Scholarsh. 2021 May;53(3):343-350. doi: 10.1111/jnu.12654. Epub 2021 Mar 23.
To provide an example of a tweet analysis for nurse researchers using Twitter in their research.
A content analysis using tweets about "heat illness + health."
Tweets were pulled from Twitter's application programming interface with premium access using Postman and the key words "heat illness + health." All data cleaning and analysis was performed in R Version 3.5.2, and the tweet set was analyzed for term frequency, sentiment, and topic modeling. Principal R packages included LDAvis, tidytext, tm, and zyuzhet.
6,317 tweets were analyzed with a date range of April 6, 2009, to December 30, 2019. The most common terms in the tweets were heat (n = 4,532), illness (n = 4,085), and health (n = 2,257). Sentiment analysis showed that the majority of tweets (55%) had a negative sentiment. Topic modeling showed that there were three topics within the tweet set: increasing impact, prevention and safety, and symptoms.
Twitter can be a useful tool for nursing researchers, serving as a viable adjunct to current research methodologies. This practical example has facilitated a deeper understanding of the social media representation of heat illness and health that can be applied to other research.
Twitter serves as a tool for collecting health information for multiple groups, ranging from clinicians and researchers to patients. By utilizing the plethora of data that comes from the platform, we can work towards developing theories and interventions related to numerous health conditions and phenomena.
提供一个使用 Twitter 进行研究的护士研究人员的推文分析示例。
使用关于“热疾病+健康”的推文进行内容分析。
使用 Postman 从 Twitter 的应用程序编程接口中提取带有高级访问权限的推文，并使用关键字“热疾病+健康”。所有数据清理和分析均在 R 版本 3.5.2 中进行，对推文集进行了术语频率、情感和主题建模分析。主要 R 包包括 LDAvis、tidytext、tm 和 zyuzhet。
分析了 2009 年 4 月 6 日至 2019 年 12 月 30 日期间的 6317 条推文。推文中最常见的术语是热（n=4532）、疾病（n=4085）和健康（n=2257）。情感分析表明，大多数推文（55%）具有负面情绪。主题建模显示，推文集中有三个主题：影响增加、预防和安全以及症状。
Twitter 可以成为护理研究人员的有用工具，是当前研究方法的可行补充。这个实际示例促进了对热疾病和健康的社交媒体表现的更深入理解，可应用于其他研究。
Twitter 是为从临床医生和研究人员到患者的多个群体收集健康信息的工具。通过利用平台上产生的大量数据，我们可以努力开发与多种健康状况和现象相关的理论和干预措施。
