Evidence map›Paper›PMID 32115571›Full record

ArticleRevista espanola de salud publica2020

[Analysis of communication in social networks of the influenza vaccine campaign in Spain].

Mª Isabel Cano Garcinuño, Sergio Arce García

Abstract read
In one paragraph

Article in Revista espanola de salud publica, 2020. 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

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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

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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.

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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

2 authors.

Mª Isabel Cano GarcinuñoServicio de Salud del Principado de Asturias (SESPA). Oviedo. España.
Sergio Arce GarcíaEscuela Superior de Ingeniería y Tecnología (ESIT). Universidad Internacional de La Rioja (UNIR). Logroño. España.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAfter arising of anti-vaccine groups and their dissemination, it is necessary to carry out communication campaigns on the benefits of vaccination aimed at citizens, and social networks are a good way to reach a large population. The objective of this article is to determine the communication on Twitter social network during the influenza vaccine campaign in 2018 in Spain.

methodsBig data methods were used to collect all tweets about the influenza vaccine during October 23 to December 15. They were determined by cluster analysis, eigenvector and pagerank calculations to determinate who were the most important influencers during the campaign.

resultsA total of 9,147 tweets were collected, of which 71.94% were retweets (RT). Ten groups generated 69.92% of the message traffic on vaccines. The main emotion expressed in the messages about vaccines is the fear of consequences if people do not get vaccinated.

conclusionsIt was determined that the information on the campaign is favorable to vaccination but is mainly directed by (supposedly) doctors, nurses or anonymous patients who tweet and are followed by many users. The official and institutional campaigns, some of which are re-disseminated in a possibly organized way, are very neglected in the monitoring of society in the networks.

Indexed as

Attitude to HealthInfluenza VaccinesSocial MediaSocial NetworkingAnti-Vaccination MovementBig DataCluster AnalysisEmotionsHealth CommunicationHumansImmunization ProgramsMachine LearningSpainInfluenza VaccinesCommunicationFluInfluencerSocial networkSpainTwitterVaccination

Identifiers

PMID32115571
PMCPMC11566982

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Registered trials

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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.