Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study.
Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study.
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DOI：
10.2196/25108
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发表时间：
2021-02-09
影响因子：
7.4
通讯作者：
Luli GK
中科院分区：
文献类型：
--
作者：
Lyu JC;Luli GK
疾病控制与预防中心（CDC）是美国的国家公共卫生保护机构。随着COVID-19大流行对美国和世界各地社会的影响不断升级，CDC已成为公众讨论的焦点之一。本研究旨在确定 Twitter 上有关 CDC 的公众与 COVID-19 相关的讨论中出现的主题及其总体主题，并进一步深入了解公众的担忧、关注焦点、对 CDC 当前表现的看法以及对 CDC 的期望。推文是从 2020 年 3 月 11 日世界卫生组织宣布 COVID-19 大流行到 2020 年 8 月 14 日的大规模 COVID-19 Twitter 聊天数据集中下载的。我们使用 R（R 基金会）来清理推文并保留包含五个特定关键字（cdc、CDC、疾病控制和预防中心、CDCgov 和 cdcgov）中任意一个的推文，同时消除所有 CDC 本身发布的 91 条推文。分析中包含的最终数据集包含来自 152,314 个不同用户的 290,764 条独特推文。我们使用 R 来执行主题建模的潜在狄利克雷分配算法。 Twitter 数据生成了 16 个主题，公众在谈论 COVID-19 时将这些主题链接到 CDC。其中，讨论最多的是 COVID-19 死亡人数，占分析中 290,764 条推文的 12.16%（n=35,347），其次是关于 CDC 和其他当局的可信度以及 CDC 的 COVID-19 指南的一般看法，每条推文均超过 20,000 条。这 16 个主题分为四个总体主题：了解病毒和情况、政策和政府行动、应对指南以及关于可信度的一般看法。 Twitter 等社交媒体平台为公众舆论提供了宝贵的数据库。在 COVID-19 等旷日持久的大流行中，快速有效地识别 Twitter 上公众讨论的主题将有助于公共卫生机构改善与公众的下一轮沟通。
The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords—cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov—while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.
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影响因子：
8.5
作者：
Li, Jiawei;Xu, Qing;Mackey, Tim
通讯作者：
Mackey, Tim
DOI：
10.1097/phh.0000000000000516
发表时间：
2017-11-01
影响因子：
3.3
作者：
Harris, Jenine K.;Hawkins, Jared B.;Brownstein, John S.
通讯作者：
Brownstein, John S.
影响因子：
7.4
作者：
Budhwani, Henna;Sun, Ruoyan
通讯作者：
Sun, Ruoyan
影响因子：
6.2
作者：
LOGES, WE
通讯作者：
LOGES, WE
影响因子：
6
作者：
Blei, DM;Ng, AY;Jordan, MI
通讯作者：
Jordan, MI