Evidence map›Paper›PMID 34581682›Full record

ArticleJournal of medical Internet research2021

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis.

Michal Monselise, Chia-Hsuan Chang, Gustavo Ferreira, Rita Yang, Christopher C Yang

Abstract 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 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
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

28 citing papers in PubMed.

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  7. Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.Journal of the American Heart Association · 2024
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  17. COVID-19 Vaccines on TikTok: A Big-Data Analysis of Entangled Discourses.International journal of environmental research and public health · 2022
    Article
  18. Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.International journal of environmental research and public health · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Michal MonseliseCollege of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID 0000-0002-6222-4822
Chia-Hsuan ChangDepartment of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan.ORCID 0000-0001-9116-8244
Gustavo FerreiraCollege of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID 0000-0001-6885-1264
Rita YangVirtua Voorhees Hospital, Voorhees Township, NJ, United States.ORCID 0000-0002-3077-1434
Christopher C YangCollege of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID 0000-0001-5463-6926

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter.

objectiveThe goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media.

methodsTo better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity.

resultsAfter removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy.

conclusionsThis research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

Indexed as

COVID-19Social MediaCOVID-19 VaccinesHumansSARS-CoV-2United StatesVaccinationCOVID-19 VaccinescommunicationconcernCOVID-19discussionemotionfearhealth care informaticshealth informationhesitancysentimentsocial mediatopic detectiontrendtrustTwitterunsupervised sentiment analysisvaccinevaccine hesitancy

Identifiers

PMID34581682
PMCPMC8534488

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.