Analysis of communication in social networks of the influenza vaccine campaign in Spain

e202003008

Authors

  • Mª Isabel Cano Garcinuño Servicio de Salud del Principado de Asturias (SESPA). Oviedo. España.
  • Sergio Arce García Escuela Superior de Ingeniería y Tecnología (ESIT). Universidad Internacional de La Rioja (UNIR). Logroño. España.

Keywords:

Vaccination, Flu, Communication, Social network, Influencer, Twitter

Abstract

Background: After 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.
Methods: Big 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.
Results: A 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.
Conclusions: It 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.

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References

OMS. Gripe. Disponible en https://www.who.int/topics/influenza/es.

Woodland DL. Building a Better Flu Vaccine. Viral Immunol 2018;31(4):277.

Comité asesor de vacunas de la AEP. Vacuna de la gripe. Disponible en https://vacunasaep.org/familias/vacunas-una-a-una/vacuna-gripe.

Eskola J, Duclos P, Schuster M, MacDonald NE. How to deal with vaccine hesitancy? Vaccine 2015;33:4215–4217.

Ohlrogge AW, Suggs LS. Flu vaccination communication in Europe: What does the government communicate and how? Vaccine 2018;36(44):6512-6519.

Ministerio de Sanidad, Consumo y Bienestar Social. Disponible en https://www.mscbs.gob.es/profesionales/saludPublica/prevPromocion/vacunaciones/home.htm .

Thomson A, Vallee-Tourangeu G, Suggs LS. Strategies to increase vaccine acceptance and uptake: from behavioral insights to context-specific, culturally-appropriate, evidence-based communications and interventions. Vaccine 2018;36(44):6457-6458.

ECDC. Seasonal influenza communication toolkit guidelines. 2016. Disponible en https://ecdc.europa.eu/en/seasonal-influenza/prevention-and-control/communication-toolkit.

Tuells J, Rodríguez-Blanco N, Duro Torrijos JL, Vila-Candel R, Nolasco Bonmati A. Vaccination of pregnant women in the Valencian Community during the 2014-15 influenza season: a multicentre study. Rev Esp Quimioter 2018;31(4):344-352.

Cuesta U, Gaspar S. La “reputación online” de la información de vacunas en internet. Historia y comunicación social 2014;19:15–29.

Cuesta U, Gaspar S. Comunicación 2.0 y salud pública: redes sociales, “influencers” y vacunas. En: Innovación universitaria: digitalización 2.0 y excelencia en contenidos. Madrid, McGraw-Hill; 2016.p.161-175.

Digital 2019 España. Madrid; We Are Social, S.L; 2019. Disponible en https://wearesocial.com/es/digital-2019-espana.

Encuesta AIMC a Usuarios de Medios de Comunicación. Madrid; Asociación para la Investigación de Medios de Comunicación, AIMC; 2018.

Miquel-Segarra S, Alonso-Muñoz, Marcos-García S. Buscando la interacción. Partidos y candidatos en Twitter durante las elecciones generales de 2015. Revista Prisma Social 2017;(18):34-54.

Quintana L, Sosa A, Castillo A. Acciones y estrategias de comunicación en plataformas digitales. El caso Cifuentes. Revista Prisma Social 2018; (22):247-270.

Cancelo M, Gadea G. Emponderamiento de las redes sociales en las crisis institucionales. Vivat Academia 2013;124:21-33.

Kearney MW. Rtweet: Collecting Twitter Data. R package version 0.6.7. Disponible en: https://cran.r-project.org/package=rtweet.

Bastian M, Heymann S, Jacomy M. “Gephi: An Open Source Software for Exploring and Manipulating Networks”. Proceedings of the Third International ICWSM Conference; 2009 17-20 mayo; San Jose: Conferencia Internacional AAAI sobre Web y Redes Sociales; 2009.

Brin S, Page L. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proceedings of the seventh International Conference on the World Wide Web;1998 14-18 Abril; Brisbane, Australia; 1998. p.107-117.

Fernández Vallejo AM. (2018). Comunicar emociones en el discurso metapolítico de twitter: el caso de #MADURO versus @NICOLASMADURO. Observatorio (OBS*) Journal 2018:175-194.

Sautera DA, Eisner F, Ekman P, Scott SK. Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations. Proc Natl Acad Sci U S A; 2010; 107(6):2408–2412.

Plutchik R. A general psychoevolutionary theory of emotion. Emotion: Theory, Research, and Experience 1980;1(3):3–33

Mohammad S, Turney P. Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon. Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text; 2010 junio; Los Angeles, California: North American Chapter of the Association for Computational Linguistics; 2010.

Published

2020-03-02

How to Cite

1.
Cano Garcinuño MI, Arce García S. Analysis of communication in social networks of the influenza vaccine campaign in Spain: e202003008. Rev Esp Salud Pública [Internet]. 2020 Mar. 2 [cited 2024 Nov. 27];94:10 páginas. Available from: https://ojs.sanidad.gob.es/index.php/resp/article/view/839