Evidence map›Paper›PMID 36560577›Full record

ArticleVaccines2022

Media Reports on COVID-19 Vaccinations: A Study of Topic Modeling in South Korea.

Keumseok Koh, Seunghyeon Lee, Sangdon Park, Jaewoo Lee

Abstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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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.

Keumseok KohDepartment of Geography, The University of Hong Kong, Room 10.31, 10F, The Jockey Club Tower, Pokfulam RD, Hong Kong, China.ORCID 0000-0001-7263-2697
Seunghyeon LeeDepartment of Industrial Security, Chung-Ang University, Heukseok-ro 84, Dongjak-gu, Seoul 06974, Republic of Korea.
Sangdon ParkAlphaSights Ltd., WeWork Eulji-ro 7th Floor 343, Samil-Daero, Jung-gu, Seoul 04538, Republic of Korea.
Jaewoo LeeDepartment of Industrial Security, Chung-Ang University, Heukseok-ro 84, Dongjak-gu, Seoul 06974, Republic of Korea.ORCID 0000-0001-5887-2184

Funding

Chung-Ang University Research Grant in 2020National Research Foundation of Korea 5199990314137The University of Hong Kong Hui Oi Chow Trust Fund
6 · The paper itself

Abstract

Early successes in controlling the COVID-19 pandemic have prevented Republic of Korea from implementing a prompt, large-scale vaccine rollout to the public. The influence of traditional media on public opinion remains critical and substantial in Republic of Korea, and there have been heated debates about vaccination in traditional media reports in Korea. Effective and efficient public health communication is integral in managing public health challenges. This study explored media reports on the COVID-19 vaccines during the pandemic in Republic of Korea. 12,399 media news reports from May 2020 to September 2021 were collected. An LDA topic model was applied in order to analyze and compare the topics drawn from each study phase using words from the unstructured text data. Although media reports from before the national vaccination implementation focused on the development and rollout of COVID-19 vaccines, diverse topics were reported without any overlap. After the vaccination rollout, the biggest concern was the side effects of the COVID-19 vaccine. In sum, Republic of Korea's major media outlets reported on diverse topics rather than generating a common discourse about topics related to COVID-19 vaccination.

Indexed as

Coronavirus disease 2019 (COVID-19)latent Dirichlet allocation (LDA) topic modelmediapublic health communicationRepublic of Koreavaccination

Identifiers

PMID36560577
PMCPMC9782437

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.