Evidence map›Paper›PMID 34115608›Full record

ArticleJournal of medical Internet research2021

COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis.

Joanne Chen Lyu, Eileen Le Han, Garving K Luli

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.

0numbers the graph read from it
0cells of the map it votes in
134citing papers in PubMed
37.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

134 citing papers in PubMed, 352 citations in OpenAlex.

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74 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Joanne Chen LyuCenter for Tobacco Control Research and Education, University of California, San Francisco, San Francisco, CA, United States.ORCID 0000-0002-4178-6746
Eileen Le HanSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-3443-4240
Garving K LuliDepartment of Mathematics, University of California, Davis, Davis, CA, United States.ORCID 0000-0002-9630-1750
University of California, Davis · USUniversity of California, San Francisco · USUniversity of Michigan · US

Funding

Postdoctoral training in tobacco controlT32CA113710 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2016 to 2020
$2.6M
NCI NIH HHS T32 CA113710
6 · The paper itself

Abstract

backgroundVaccination 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.

objectiveThe 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.

methodsTweets 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.

resultsTopic 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.

conclusionsPublic 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.

Indexed as

COVID-19COVID-19 VaccinesEmotionsLatent Class AnalysisSocial MediaTrustHumansImmunity, HerdNatural Language ProcessingPandemicsSARS-CoV-2VaccinationCOVID-19 VaccinescommunicationconcernCOVID-19discussionemotioninfodemiologyinfoveillanceopinionperceptionsentimentsocial mediatopicTwittervaccinationvaccine

Identifiers

PMID34115608
PMCPMC8244724
OpenAlexW3171261489

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.