Evidence map›Paper›PMID 40188270›Full record

ArticleScientific reports2025

Twitter communities are associated with changing user's opinion towards COVID-19 vaccine in Japan.

Qianyun Wu, Yukie Sano, Hideki Takayasu, Shlomo Havlin, Misako Takayasu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Qianyun WuSchool of Computing, Institute of Science Tokyo, Yokohama, 226-8501, Japan.
Yukie SanoInstitute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan.
Hideki TakayasuSchool of Computing, Institute of Science Tokyo, Yokohama, 226-8501, Japan.
Shlomo HavlinSchool of Computing, Institute of Science Tokyo, Yokohama, 226-8501, Japan. havlins@gmail.com.
Misako TakayasuSchool of Computing, Institute of Science Tokyo, Yokohama, 226-8501, Japan. takayasu@comp.isct.ac.jp.

Funding

Institute of Science Tokyo Research Project for Overcoming Coronavirus Disasters LaunchedJapan Society for the Promotion of Science 23K28192
6 · The paper itself

Abstract

Despite extensive research studying the opinions on vaccination since the outbreak of COVID-19, the dynamics of temporal opinion shifts of individuals and their possible origins have been rarely studied. Here, we explore the possible influence of social interactions (retweet network) on individual's opinion shift related to vaccinations based on large-scale Twitter data in Japan. We use an opinion score which calculates the fraction of pro-, neutral and anti-vaccine tweets to measure the dynamic changes of individual opinions, and identify statistically significant communities based on retweet network. By tracking individual's dynamic opinion and its community affiliation, our study highly suggests that the opinion shifts are largely influenced by the user's Twitter community. That is, if users are within the anti-vaccine (or pro-vaccine) community, they exhibit a significantly higher likelihood of changing their position and adopt an anti-vaccine (or pro-vaccine) stance. We also find that the anti-vaccine community's influence appears to persist longer than the pro-vaccine community.

Indexed as

COVID-19COVID-19 VaccinesPublic OpinionSocial MediaVaccinationHumansJapanSARS-CoV-2COVID-19 VaccinesCommunity detectionOpinion polarizationSocial mediaSocial networks

Identifiers

PMID40188270
PMCPMC11972375

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.