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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Oct 25, 2020
Date Accepted: Jan 25, 2021
Date Submitted to PubMed: Jan 26, 2021

The final, peer-reviewed published version of this preprint can be found here:

Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

LYU JC, Luli GK

Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

J Med Internet Res 2021;23(2):e25108

DOI: 10.2196/25108

PMID: 33497351

PMCID: 7879718

Understanding the Public Discussion about the CDC in the COVID-19 Pandemic: A text-mining analysis of Twitter data

  • Joanne Chen LYU; 
  • Garving Kevin Luli

ABSTRACT

Background:

The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the US. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, CDC has become one of the focal points of public discussion.

Objective:

This study aims to identify the topics and the overarching themes of the topics emerging from the public COVID-19-related discussion on Twitter about the CDC, and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC.

Methods:

A total of 128,432,021 tweets without retweets were downloaded from a large-scale COVID-19 Twitter chatter dataset from March 11, 2020, when the WHO declared COVID-19 a pandemic, to August 14, 2020. We used R to clean the tweets and retained only tweets that contain any of the following keywords ("cdc", "CDC", "centers for disease control and prevention", "CDCgov", "cdcgov"), while eliminating all 91 tweets by CDC itself. The final data included in the analysis consists of 290,764 unique tweets from 152,314 different users. We used the Latent Dirichlet Allocation (LDA) algorithm for topic modeling in R.

Results:

The Twitter data generated sixteen 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% of the total 290,764 tweets in the analysis, followed by general opinions about credibility of the CDC and other authorities, and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The sixteen topics fall into four overarching themes: knowing the virus and the situation, general opinion about credibility, response guidelines, and policy and government actions.

Conclusions:

Social media like Twitter provide a valuable database for public opinion. In a protracted pandemic like the COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies to understand the public's concerns, focus of attention, and expectation from them and further provide insight for the next-round communication with the public.


 Citation

Please cite as:

LYU JC, Luli GK

Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

J Med Internet Res 2021;23(2):e25108

DOI: 10.2196/25108

PMID: 33497351

PMCID: 7879718

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