College of Nursing and Public Health, Adelphi University, Garden City, NY, USA.
School of Engineering and Applied Sciences, University at Buffalo, SUNY, Buffalo, NY, USA.
Public Health Nurs. 2021 Mar;38(2):145-151. doi: 10.1111/phn.12843. Epub 2020 Nov 30.
Efforts to control the current coronavirus disease 2019 (COVID-19) pandemic have led to national lockdowns around the world. Reactions to the rapidly evolving outbreak were shared on social media platforms. We conducted a mixed-methods analysis of tweets collected from May 10 to May 24, 2020, using MAXQDA software in conjunction with Twitters search API using the keywords: "COVID-19," "coronavirus pandemic," "Covid19," "face masks," and included terms such as "Queens," "Bronx," "New York." A total of 7, 301 COVID-19-related tweets across the globe were analyzed. We used SAS Text Miner V.15.1 for descriptive text mining to uncover the primary topics in unstructured textual data. Content analysis of tweets revealed six themes: surveillance, prevention, treatments, testing and cure, symptoms and transmission, fear, and financial loss. Our study also demonstrates the feasibility of using Twitter to capture real-time data to assess the public's concerns and public health needs during the COVID-19 pandemic.
为控制当前 2019 年冠状病毒病（COVID-19）大流行，全球各国纷纷实行封锁措施。人们在社交媒体平台上分享了对这一迅速演变的疫情的反应。我们使用 MAXQDA 软件结合 Twitter 的搜索 API，于 2020 年 5 月 10 日至 5 月 24 日期间，对收集到的推文进行了混合方法分析，使用的关键词为：“COVID-19”“冠状病毒大流行”“Covid19”“口罩”，并包括“皇后区”“布朗克斯”“纽约”等术语。分析了全球范围内共 7301 条与 COVID-19 相关的推文。我们使用 SAS Text Miner V.15.1 进行描述性文本挖掘，以揭示非结构化文本数据中的主要主题。推文的内容分析揭示了六个主题：监测、预防、治疗、检测和治疗、症状和传播、恐惧和经济损失。我们的研究还表明，使用 Twitter 捕获实时数据以评估 COVID-19 大流行期间公众关注和公共卫生需求是可行的。
