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COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis.
Lyu, Joanne Chen; Han, Eileen Le; Luli, Garving K.
  • Lyu JC; Center for Tobacco Control Research and Education, University of California, San Francisco, San Francisco, CA, United States.
  • Han EL; School of Information, University of Michigan, Ann Arbor, MI, United States.
  • Luli GK; Department of Mathematics, University of California, Davis, Davis, CA, United States.
J Med Internet Res ; 23(6): e24435, 2021 06 29.
Article in English | MEDLINE | ID: covidwho-1286902
ABSTRACT

BACKGROUND:

Vaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines.

OBJECTIVE:

The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine-related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals.

METHODS:

Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon.

RESULTS:

Topic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world's first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective.

CONCLUSIONS:

Public COVID-19 vaccine-related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccination / Trust / Emotions / Social Media / Latent Class Analysis / COVID-19 Vaccines / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 24435

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccination / Trust / Emotions / Social Media / Latent Class Analysis / COVID-19 Vaccines / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 24435