Evidence map›Paper›PMID 34346885›Full record

ArticleJournal of medical Internet research2021

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach.

Chen Luo, Kaiyuan Ji, Yulong Tang, Zhiyuan Du

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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

4 authors.

Chen Luo *School of Journalism and Communication, Tsinghua University, Beijing, China.ORCID 0000-0002-9736-0533
Kaiyuan Ji *School of Journalism and Communication, Tsinghua University, Beijing, China.ORCID 0000-0002-0554-3288
Yulong TangInstitute of Communication Studies, Communication University of China, Beijing, China.ORCID 0000-0001-8243-6323
Zhiyuan DuSchool of Journalism and Communication, Tsinghua University, Beijing, China.ORCID 0000-0002-8555-7216

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines.

objectiveThis study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context.

methodsWe collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups.

resultsLaypeople are more prevailing in the COVID-19 vaccine-related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the "safety of Chinese-made vaccine" topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference.

conclusionsOur findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as "qualitatively different" in perspective rather than "quantitatively different" in scientific knowledge. Among those questions closely associated with medical expertise, the "qualitatively different" characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.

Indexed as

COVID-19VaccinesCOVID-19 VaccinesData MiningHumansSARS-CoV-2VaccinationCOVID-19 VaccinesVaccinesadverse reactionsCOVID-19laypeoplemedical professionalstructural topic modelingvaccinationvaccinevaccine developmentvaccine effectivenessZhihu

Identifiers

PMID34346885
PMCPMC8404777

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.