Comunicación en redes sociales en escenarios de pandemia o epidemia: un análisis bibliométrico

Social media communication in pandemic or epidemic scenarios: a bibliometric analysis

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Carlos Osorio Andrade Universidad del Valle, Cali, Colombia
Carlos Alberto Arango Pastrana Universidad del Valle, Cali, Colombia
Ana Jiménez Zarco Universitat Oberta de Catalunya, Barcelona, España.
Resumen

Objetivo. Realizar una revisión de literatura de 165 artículos científicos publicados en Scopus que abordan el papel de las redes sociales en escenarios de pandemia o epidemia. Metodología. Se utilizó la bibliometría para extraer indicadores de literatura y mapas que evidencian corrientes de investigación y palabras más frecuentes. Resultados. El análisis bibliométrico permitió identificar un crecimiento significativo del tema, el cual coincide con
la primera ola del coronavirus en Europa y América. De igual manera se identifica que gran parte de los estudios se enfocan en analizar el tipo de información que se divulga sobre la COVID-19 en redes sociales. Conclusiones. Esta investigación señala la importancia de adelantar futuros estudios en contextos latinoamericanos; asimismo, plantea la necesidad de examinar el impacto psicológico del uso de medios de comunicación en escenarios
pandémicos; por último, es importante ahondar en estrategias que permitan mejorar la comunicación pública en situaciones de emergencia sanitaria.

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Biografía del autor/a / Ver

Carlos Osorio Andrade, Universidad del Valle, Cali, Colombia

Magíster en Ciencias de la Organización. Universidad del Valle, Cali, Colombia.

Carlos Alberto Arango Pastrana, Universidad del Valle, Cali, Colombia

Doctor en Organización Industrial y Gestión de Empresas. Universidad del Valle, Cali, Colombia

Ana Jiménez Zarco , Universitat Oberta de Catalunya, Barcelona, España.

Doctora en Economía y Empresa. Universitat Oberta de Catalunya, Barcelona, España.

Referencias

Abd-Alrazaq, A. et al. (2020). Top concerns of tweeters during the COVID-19 pandemic: A surveillance study. Journal of Medical Internet Research, 22(4), 1-9.

https://doi.org/10.2196/19016 DOI: https://doi.org/10.2196/19016

Azim, D. et al. (2020). Media on the frontline against mental health implications of COVID-19 in Pakistan. Asian Journal of Psychiatry, 54, 102342.

https://doi.org/10.1016/j.ajp.2020.102342 DOI: https://doi.org/10.1016/j.ajp.2020.102342

Basch, C.H. et al. (2015). Coverage of the Ebola Virus Disease Epidemic on YouTube. Disaster Medicine and Public Health Preparedness, 9(5), 531-535.

https://doi.org/10.1017/dmp.2015.77 DOI: https://doi.org/10.1017/dmp.2015.77

Bavel, J.J.V. et al. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4(5), 460-471.

https://doi.org/10.1038/s41562-020-0884-z DOI: https://doi.org/10.1038/s41562-020-0884-z

Boeris, C. (2010). Aplicación de métodos bibliométricos a la evaluación de colecciones: el caso de la Biblioteca del Instituto Argentino de Radioastronomía. Recuperado de http://sedici. unlp.edu.ar/handle/10915/17179.

Chao, M. et al. (2020). Media use and acute psychological outcomes during COVID-19 outbreak in China. Journal of Anxiety Disorders, 74, 1-8.

https://doi.org/10.1016/j.janxdis.2020.102248 DOI: https://doi.org/10.1016/j.janxdis.2020.102248

Chapman, H.J. et al. (2016). Addressing the role of medical students using community mobilization and social media in the Ebola response. Perspectives on Medical Education, 5(3), 186-190. https://doi.org/10.1007/s40037-016-0271-7 DOI: https://doi.org/10.1007/S40037-016-0271-7

Chew, C. and Eysenbach, G. (2010). Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLOS ONE, 5(11), 1-13.

https://doi.org/10.1371/journal.pone.0014118 DOI: https://doi.org/10.1371/journal.pone.0014118

Daim, T. et al. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981-1012.

https://doi.org/10.1016/j.techfore.2006.04.004 DOI: https://doi.org/10.1016/j.techfore.2006.04.004

Dickmann, P. et al. (2015). Making sense of communication interventions in public health emergencies - an evaluation framework for risk communication. Journal of Communication in Healthcare, 8(3), 233-240. https://doi.org/10.1080/17538068.2015.1101962 DOI: https://doi.org/10.1080/17538068.2015.1101962

Fong, S., Dey, N. and Chaki, J. (2020). An Introduction to COVID-19. En Fong, S., Dey, N. and Chaki, J. (Ed.), Artificial Intelligence for Coronavirus Outbreak (pp. 1-22). Warszawa, Poland: Springer. https://doi.org/10.1007/978-981-15-5936-5_1 DOI: https://doi.org/10.1007/978-981-15-5936-5_1

Fu, K. et al. (2016). How people react to Zika virus outbreaks on Twitter? A computational content analysis. American Journal of Infection Control, 44(12), 1700-1702. https://doi.org/10.1016/j.ajic.2016.04.253 DOI: https://doi.org/10.1016/j.ajic.2016.04.253

Fung, I. et al. (2016). Social Media's initial reaction to information and misinformation on Ebola, august 2014: facts and rumors. Public Health Reports, 131(3), 461-473. https://doi.org/10.1177/003335491613100312 DOI: https://doi.org/10.1177/003335491613100312

Fung, I. et al. (2017). Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study. Infection, Disease and Health, 23(1), 10-16.

https://doi.org/10.1016/j.idh.2017.08.005 DOI: https://doi.org/10.1016/j.idh.2017.08.005

Gupta, L. et al. (2020). Information and misinformation on COVID-19: A cross-sectional survey study. Journal of Korean Medical Science, 35(27), 1-11.

https://doi.org/10.3346/jkms.2020.35.e256 DOI: https://doi.org/10.3346/jkms.2020.35.e256

Jacobsen, K.H. et al. (2016). Lessons from the ebola outbreak: Action items for emerging infectious disease preparedness and response. EcoHealth, 13(1), 200-212.

https://doi.org/10.1007/s10393-016-1100-5 DOI: https://doi.org/10.1007/s10393-016-1100-5

Jit, M. et al. (2015). Building a new communication paradigm: Can we influence influenza perception? Vaccine, 33(49), 7044-7046.

https://doi.org/10.1016/j.vaccine.2015.08.051 DOI: https://doi.org/10.1016/j.vaccine.2015.08.051

Karafillakis, E. and Larson, H.J. (2017). The benefit of the doubt or doubts over benefits? A systematic literature review of perceived risks of vaccines in European populations. Vaccine, 35(37), 4840-4850.

https://doi.org/10.1016/j.vaccine.2017.07.061 DOI: https://doi.org/10.1016/j.vaccine.2017.07.061

Kass, T. and Alhinnawi, H. (2013). Social media in public health. British Medical Bulletin, 108(1), 5-24.

https://doi.org/10.1093/bmb/ldt028 DOI: https://doi.org/10.1093/bmb/ldt028

Kostkova, P., de Quincey, E. and Jawaheer, G. (2010). The potential of social networks for early warning and outbreak detection systems: the swine flu Twitter study. International Journal of Infectious Diseases, 14(1), e384-e385. https://doi.org/10.1016/j.ijid.2010.02.475 DOI: https://doi.org/10.1016/j.ijid.2010.02.475

Kullar, R. et al. (2020). To Tweet or Not to Tweet-a Review of the Viral Power of Twitter for Infectious Diseases. Current Infectious Disease Reports, 22(14), 1-6.

https://doi.org/10.1007/s11908-020-00723-0 DOI: https://doi.org/10.1007/s11908-020-00723-0

La, V.P. (2020). Policy response, social media and science journalism for the sustainability of the public health system amid the COVID-19 outbreak: The vietnam lessons. Sustainability, 12(7), 2931. https://doi.org/10.3390/su12072931 DOI: https://doi.org/10.3390/su12072931

Lancho, B. and Cantú, F. (2019). Science in Mexico: a bibliometric analysis. Scientometrics, 118(2), 499-517.

https://doi.org/10.1007/s11192-018-2985-2 DOI: https://doi.org/10.1007/s11192-018-2985-2

Li, C. et al. (2020). Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance, 25(10), 2000199.

https://doi.org/10.2807/1560-7917.ES.2020.25.10.2000199 DOI: https://doi.org/10.2807/1560-7917.ES.2020.25.10.2000199

Li, J., Lei, L. and Cheng, L. (2020). Mapping Evaluation, Appraisal and Stance in Discourse (2000-2015): A Bibliometric Analysis. Glottotheory, 10(1-2), 31-55.

https://doi.org/10.1515/glot-2019-0002 DOI: https://doi.org/10.1515/glot-2019-0002

Liu, K. et al. (2016). Chinese public attention to the outbreak of ebola in west africa: Evidence from the online big data platform. International Journal of Environmental Research and Public Health, 13(8), 1-15. https://doi.org/10.3390/ijerph13080780 DOI: https://doi.org/10.3390/ijerph13080780

Moorhead, S.A. et al. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of MedicalInternet Research, 15(4), e85. https://doi.org/10.2196/jmir.1933 DOI: https://doi.org/10.2196/jmir.1933

Novelli, M. et al. (2018). 'No Ebola…still doomed' - The Ebola-induced tourism crisis. Annals ofTourism Research, 70, 76-87.

https://doi.org/10.1016/j.annals.2018.03.006 DOI: https://doi.org/10.1016/j.annals.2018.03.006

Odlum, M. and Yoon, S. (2015). What can we learn about the Ebola outbreak from tweets? American Journal of Infection Control, 43(6), 563-571.

https://doi.org/10.1016/j.ajic.2015.02.023 DOI: https://doi.org/10.1016/j.ajic.2015.02.023

Odriozola, I., Berbegal, J. and Merigó, J. (2019). Open innovation in small and medium enterprises: a bibliometric analysis. Journal of Organizational Change Management, 32(5), 533-557.

https://doi.org/10.1108/JOCM-12-2017-0491 DOI: https://doi.org/10.1108/JOCM-12-2017-0491

Oh, S.H., Lee, S.Y. and Han, C. (2020). The effects of social media use on preventive behaviors during infectious disease outbreaks: The mediating role of self-relevant emotions and public risk perception. Health Communication, 36(8), 972-981. Oyeyemi, S.O., Gabarron, E. and Wynn, R. (2014).

https://doi.org/10.1080/10410236.2020.1724639 DOI: https://doi.org/10.1080/10410236.2020.1724639

Ebola, Twitter, and misinformation: A dangerous combination? BMJ, 349, 14-15.

Peñasco, R. (2020). Covid19: ¿un antes y un después en la Historia de la Humanidad? Análisis sociojurídico de un cambio de paradigma y de los nuevos parámetros y grandes retos del siglo XXI. Madrid, España: Dykinson. https://doi.org/10.2307/j.ctv1503k85 DOI: https://doi.org/10.2307/j.ctv1503k85

Pérez, J.-A., Meso, K. y Mendiguren, T. (2020). Fake news y coronavirus: detección de los principales actores y tendencias a través del análisis de las conversaciones en Twitter. El Profesional de la Información, 29(3), 1-22. https://doi.org/10.3145/epi.2020.may.08 DOI: https://doi.org/10.3145/epi.2020.may.08

Raven, J., Wurie, H. and Witter, S. (2018). Health workers' experiences of coping with the Ebola epidemic in Sierra Leone's health system: a qualitative study. BMC Health Services Research, 18(251). https://doi.org/10.1186/s12913-018-3072-3 DOI: https://doi.org/10.1186/s12913-018-3072-3

Rodríguez, A., Osorio, C. y Peláez, J. (2019). Dos décadas de investigación en Electronic Word of Mouth: un análisis bibliométrico. Pensamiento y Gestión, 48, 251-275.

Rodríguez-Soler, R., Uribe-Toril, J. and Valenciano, J.D.P. (2020). Worldwide trends in the scientific production on rural depopulation, a bibliometric analysis using bibliometrix R-tool. Land Use Policy, 97, 104787. https://doi.org/10.1016/j.landusepol.2020.104787 DOI: https://doi.org/10.1016/j.landusepol.2020.104787

Rosenberg, H., Syed, S. and Rezaie, S. (2020). The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic. Canadian Journal of Emergency Medicine, 22(4), 418-421. https://doi.org/10.1017/cem.2020.361 DOI: https://doi.org/10.1017/cem.2020.361

Salathé, M. et al. (2013). Influenza A(H7N9) and the Importance of Digital Epidemiology. New England Journal of Medicine, 369(5), 401-404.

https://doi.org/10.1056/NEJMp1307752 DOI: https://doi.org/10.1056/NEJMp1307752

Salathé, M. and Khandelwal, S. (2011). Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control. PLOS Computational Biology, 7(10), 1-8. https://doi.org/10.1371/journal.pcbi.1002199 DOI: https://doi.org/10.1371/journal.pcbi.1002199

Sastry, S. and Lovari, A. (2017). Communicating the Ontological Narrative of Ebola: An Emerging Disease in the Time of "Epidemic 2.0." Health Communication, 32(3), 329-338. https://doi.org/10.1080/10410236.2016.1138380 DOI: https://doi.org/10.1080/10410236.2016.1138380

Schimmenti, A., Billieux, J. and Starcevic, V. (2020). The four horsemen of fear during the COVID pandemic. Clinical Neuropsychiatry, 17(2), 41-45.

Seltzer, E.K. et al. (2017). Public sentiment and discourse about Zika virus on Instagram. Public Health, 150(215), 170-175.

https://doi.org/10.1016/j.puhe.2017.07.015 DOI: https://doi.org/10.1016/j.puhe.2017.07.015

Shimizu, K. (2020). 2019-nCoV, fake news, and racism. The Lancet, 395, 685-686.

https://doi.org/10.1016/S0140-6736(20)30357-3 DOI: https://doi.org/10.1016/S0140-6736(20)30357-3

Siso, R.L.V. et al. (2020). La Unión Europea ante la desinformación y las fake news. El fact checking como un recurso de detección, prevención y análisis. En Vicente, A. y Sierra, J. (Ed.),

Aproximación periodística y educomunicativa al fenómeno de las redes sociales (pp. 985-1002). Madrid, España: McGraw-Hill.

Strekalova, Y.A. (2016). Health Risk Information Engagement and Amplification on Social Media: News About an Emerging Pandemic on Facebook. Health Education and Behavior, 44(2), 332-339. https://doi.org/10.1177/1090198116660310 DOI: https://doi.org/10.1177/1090198116660310

Tang, L. et al. (2018). Social media and outbreaks of emerging infectious diseases: A systematic review of literature. American Journal of Infection Control, 46(9), 962-972. https://doi.org/10.1016/j.ajic.2018.02.010 DOI: https://doi.org/10.1016/j.ajic.2018.02.010

Uribe, J. et al. (2019). Corruption and entrepreneurship: A bibliometric analysis. Journal of Legal, Ethical and Regulatory Issues, 22(4), 1-11.

van Eck, N. and Waltman, L. (2014). Visualizing Bibliometric Networks. En Ding, Y., Rousseau, R. and Wolfram, D. (Ed.), Measuring Scholarly Impact. New York, USA: Springer. https://doi.org/10.1007/978-3-319-10377-8_13 DOI: https://doi.org/10.1007/978-3-319-10377-8_13

van Eck, N. and Waltman, L. (2019). Manual for VOSviwer version 1.6.10. Recuperado de https://www.vosviewer.com/documentation/ Manual_VOSviewer_1.6.10.pdf.

Vijaykumar, S. et al. (2018). Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter. American Journal of Infection Control, 46(5), 549-557. https://doi.org/10.1016/j.ajic.2017.10.015 DOI: https://doi.org/10.1016/j.ajic.2017.10.015

Wong, R. et al. (2017). Local Health Departments Tweeting about Ebola: Characteristics and Messaging. Journal of Public Health Management and Practice, 23(2), e16-e24. https://doi.org/10.1097/PHH.0000000000000342 DOI: https://doi.org/10.1097/PHH.0000000000000342

Wong, M. and Jensen, O. (2020). The paradox of trust: perceived risk and public compliance during the COVID-19 pandemic in Singapore. Journal of Risk Research, 23(7-8), 1021-1030. https://doi.org/10.1080/13669877.2020.1756386 DOI: https://doi.org/10.1080/13669877.2020.1756386

Yu, M. et al. (2020). Communication related health crisis on social media: a case of COVID-19 outbreak. Current Issues in Tourism, 24(19), 2699-2705.

https://doi.org/10.1080/13683500.2020.1752632 DOI: https://doi.org/10.1080/13683500.2020.1752632

Zhu, B. et al. (2020). Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons and Fractals, 140, 110123. https://doi.org/10.1016/j.chaos.2020.110123 DOI: https://doi.org/10.1016/j.chaos.2020.110123

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