Evidence map›Paper›PMID 36016126›Full record

ArticleVaccines2022

Bots' Activity on COVID-19 Pro and Anti-Vaccination Networks: Analysis of Spanish-Written Messages on Twitter.

Carlos Ruiz-Núñez, Sergio Segado-Fernández, Beatriz Jiménez-Gómez, Pedro Jesús Jiménez Hidalgo, Carlos Santiago Romero Magdalena, María Del Carmen Águila Pollo, Azucena Santillán-Garcia, Ivan Herrera-Peco

Open access · goldAbstract 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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
7.9field-weighted citation impact, top 3% 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

9 citing papers in PubMed, 20 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

8 authors at 4 institutions in 1 country.

Carlos Ruiz-NúñezPhD Program in Biomedicine, Translational Research and New Health Technologies, School of Medicine, University of Malaga, Blvr. Louis Pasteur, 29010 Málaga, Spain.ORCID 0000-0003-2872-1494
Sergio Segado-FernándezNursing Department, Faculty of Medicine, Universidad Alfonso X el Sabio, Avda Universidad, 1, Villanueva de la Cañada, 28691 Madrid, Spain.ORCID 0000-0003-3467-3556
Beatriz Jiménez-GómezNursing Department, Faculty of Medicine, Universidad Alfonso X el Sabio, Avda Universidad, 1, Villanueva de la Cañada, 28691 Madrid, Spain.
Pedro Jesús Jiménez HidalgoTraumatology and Orthopedic Surgery Service, Hospital Universitario de Móstoles, C/Dr. Luis Montes s/n., 28935 Madrid, Spain.ORCID 0000-0002-6803-0260
Carlos Santiago Romero MagdalenaFaculty of Health Sciences, Universidad Alfonso X el Sabio, Avda Universidad, 1, Villanueva de la Cañada, 28691 Madrid, Spain.ORCID 0000-0002-3642-3422
María Del Carmen Águila PolloNursing Department, Faculty of Medicine, Universidad Alfonso X el Sabio, Avda Universidad, 1, Villanueva de la Cañada, 28691 Madrid, Spain.
Azucena Santillán-GarciaValencia International University, C/Pintor Sorolla 21, 46002 Valencia, Spain.ORCID 0000-0002-7376-4170
Ivan Herrera-PecoNursing Department, Faculty of Medicine, Universidad Alfonso X el Sabio, Avda Universidad, 1, Villanueva de la Cañada, 28691 Madrid, Spain.ORCID 0000-0002-5183-5679
Universidad Alfonso X el Sabio · ESHospital Universitario de Móstoles · ESUniversidad de Málaga · ESValencian International University · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to analyze the role of bots in the dissemination of health information, both in favor of and opposing vaccination against COVID-19. Study design: An observational, retrospective, time-limited study was proposed, in which activity on the social network Twitter was analyzed. Methods: Data related to pro-vaccination and anti-vaccination networks were compiled from 24 December 2020 to 30 April 2021 and analyzed using the software NodeXL and Botometer. The analyzed tweets were written in Spanish, including keywords that allow identifying the message and focusing on bots’ activity and their influence on both networks. Results: In the pro-vaccination network, 404 bots were found (14.31% of the total number of users), located mainly in Chile (37.87%) and Spain (14.36%). The anti-vaccination network bots represented 16.19% of the total users and were mainly located in Spain (8.09%) and Argentina (6.25%). The pro-vaccination bots generated greater impact than bots in the anti-vaccination network (p < 0.000). With respect to the bots’ influence, the pro-vaccination network did have a significant influence compared to the activity of human users (p < 0.000). Conclusions: This study provides information on bots’ activity in pro- and anti-vaccination networks in Spanish, within the context of the COVID-19 pandemic on Twitter. It is found that bots in the pro-vaccination network influence the dissemination of the pro-vaccination message, as opposed to those in the anti-vaccination network. We consider that this information could provide guidance on how to enhance the dissemination of public health campaigns, but also to combat the spread of health misinformation on social media.

Indexed as

botsCOVID-19misinformationpublic healthsocial mediavaccines

Identifiers

PMID36016126
PMCPMC9414970
OpenAlexW4289519798

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.