Evidence map›Paper›PMID 35462735›Full record

ArticleJMIR infodemiology

Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts.

Ranganathan Chandrasekaran, Rashi Desai, Harsh Shah, Vivek Kumar, Evangelos Moustakas

Open access · goldAbstract read
In one paragraph

Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.9field-weighted citation impact, top 4% 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

11 citing papers in PubMed, 30 citations in OpenAlex.

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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 at 1 institution in 2 countries.

Ranganathan ChandrasekaranDepartment of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States.ORCID https://orcid.org/0000-0003-2001-578X
Rashi DesaiDepartment of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States.ORCID https://orcid.org/0000-0001-9954-6617
Harsh ShahDepartment of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States.ORCID https://orcid.org/0000-0003-1587-3069
Vivek KumarDepartment of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States.ORCID https://orcid.org/0000-0002-9638-3004
Evangelos MoustakasMiddlesex University Dubai United Arab Emirates.ORCID https://orcid.org/0000-0002-2671-9035
University of Illinois Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine-related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective: The aim of this study was to examine key themes and topics from COVID-19 vaccine-related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods: We gathered and assessed a corpus of 2.94 million COVID-19 vaccine-related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results: Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics Conclusions: Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.

Indexed as

content analysiscoronavirusCOVID-19infoveillancesentiment analysissocial mediatext miningtopic modelingtweetsTwitter studyvaccination

Identifiers

PMID35462735
PMCPMC9014796
OpenAlexW4224441261

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.