Noticias falsas y su impacto en la sociedad en épocas de pandemia

Seminario Córdova, Renzo Antonio Noticias falsas y su impacto en la sociedad en épocas de pandemia. Social Innova Sciences, 2021, vol. 2, n. 2, pp. 6-17. [Journal article (Paginated)]

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English abstract

The objective of this article was to analyze the impact of fake news on people's behavior during the time of the COVID-19 pandemic. For this, a systematic review of information was carried out, which consisted of analyzing bibliography related to fake news. The bibliographic sources were taken from databases such as Ebsco Host, Springer, Emerald Insights, Scopus, Scielo and Google Scholar. The range of years of publication was not limited, accepting as valid, articles until the year 2021. The filters used and the keywords that were taken into account were "false news", "fake news", "false news COVID-19" , “fake news and social networks”, “fake news and society”, the most important inclusion criteria being that the article focuses on the relationship between fake news and the current COVID-19 pandemic. A total of 47 bibliographic references were obtained, which coincide in the fact that the main source of proliferation and dissemination of fake news -which, currently, have generated feelings of fear and doubt in the population regarding the current pandemic — are the social networks, since they do not carry out proper control of the information that is shared on them. Therefore, it is concluded that the fake news spread by social networks have a negative influence on users, increasing their fear and leading them to make decisions that in many cases can threaten their health, therefore, the different platforms of The main social networks must implement efficient control systems for the information that is shared in them, so as to stop its dissemination.

Spanish abstract

El objetivo de este artículo fue analizar el impacto de las fake news en el comportamiento de las personas durante la época de pandemia por la COVID-19. Para ello, se realizó una revisión sistemática de información, la cual consistió en analizar bibliografía relacionada con las fake news. Las fuentes bibliográficas fueron tomadas de bases de datos como Ebsco Host, Springer, Emerald Insights, Scopus, Scielo y Google Scholar. No se limitó el rango de años de publicación, aceptándose como válidos, artículos hasta el año 2021. Los filtros utilizados y las palabras clave que se tuvieron en cuenta fueron “noticias falsas”, “fake news”, “noticias falsas COVID-19”, “fake news y redes sociales”, “fake news y sociedad”, siendo el criterio de inclusión más importante que el artículo se centre en la relación de las fake news y la actual pandemia por la COVID-19. Se obtuvo un total de 47 referencias bibliográficas, las cuales coinciden en el hecho de que la principal fuente de proliferación y difusión de fake news —las cuales, actualmente, han generado en la población, sentimientos de temor y duda con respecto a la actual pandemia— son las redes sociales, ya que estas no realizan un debido control a la información que en ellas se comparte. Por lo tanto, se concluye que, las fake news difundidas por las redes sociales influyen de manera negativa en los usuarios, aumentando su temor y llevándolos a tomar decisiones que en muchos casos pueden atentar contra su salud, por lo cual, las diferentes plataformas de las principales redes sociales, deben implementar sistemas de control eficiente a la información que se comparte en ellas, de manera que se frene su difusión.

Item type: Journal article (Paginated)
Keywords: COVID-19, Estrés Transaccional, Noticias Falsas, Red Social, Sociedad
Subjects: G. Industry, profession and education.
Depositing user: Social Innova Sciences
Date deposited: 04 May 2022 17:20
Last modified: 04 May 2022 17:20
URI: http://hdl.handle.net/10760/43145

References

Abroms, L., Lee Westmaas, J., Bontemps-Jones, J., Ramani, R. & Mellerson, J. (2013). A Content Analysis of Popular Smartphone Apps for Smoking Cessation. American Journal of Preventive Medicine, 45(6), 732-736. https://doi.org/10.1016/j.amepre.2013.07.008

Aggarwal, A., Chauhan, A., Kumar, D., Mittal, M. & Verma, S. (2020). Classification of Fake news by Fine-tuning Deep Bidirectional Transformers based Language Model. ICST Transactions on Scalable Information Systems, 7(27), 163973. https://doi.org/10.4108/eai.13-7-2018.163973

Al-Dwaikat, T., Aldalaykeh, M., Ta’an, W. & Rababa, M. (2020). The relationship between social networking sites usage and psychological distress among undergraduate students during COVID-19 lockdown. Heliyon, 6(12), e05695. https://doi.org/10.1016/j.heliyon.2020.e05695

Aldwairi, M. & Alwahedi, A. (2018). Detecting Fake news in Social Media Networks. Procedia Computer Science, 141, 215-222. https://doi.org/10.1016/j.procs.2018.10.171

Allcott, H. & Gentzkow, M. (2017). Social Media and Fake news in the 2016 Election. Journal of Economic Perspectives, 31(2), 211-236. https://doi.org/10.1257/jep.31.2.211

Appel, G., Grewal, L., Hadi, R. & Stephen, A. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79-95. https://doi.org/10.1007/s11747-019-00695-1

Apuke, O. & Omar, B. (2020). Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics and Informatics, 56, 101475. https://doi.org/10.1016/j. tele.2020.101475

Aull, B., Kuijpers, D., Sawaya, A. y Vallöf, R. (2020). What Food Retailers Should Do During the Coronavirus Crisis. McKinsey & Company, March, 5. https://www.mckinsey.com/industries/retail/our-insights/what-food-retailersshould-do-during-the-coronavirus-crisis

Ayoub, J., Yang, X. & Zhou, F. (2021). Combat COVID-19 infodemic using explainable natural language processing models. Information Processing & Management, 58(4), 102569. https://doi.org/10.1016/j.ipm.2021.102569

Bermes, A. (2021). Information overload and fake news sharing: A transactional stress perspective exploring the mitigating role of consumers’ resilience during COVID-19. Journal of Retailing and Consumer Services, 61(April), 102555. https://doi.org/10.1016/j.jretconser.2021.102555

Bonet-Jover, A., Piad-Morffis, A., Saquete, E., Martínez-Barco, P. & Ángel García-Cumbreras, M. (2021). Exploiting discourse structure of traditional digital media to enhance automatic fake news detection. Expert Systems with Applications, 169, 114340. https://doi.org/10.1016/j.eswa.2020.114340

Denyer, D. & Tranfield, D. (2009). Producing a systematic review. En Buchanan, D. & A. Bryman (Eds.), The Sage handbook of organizational research methods (671-689). Sage Publications Ltd. https://gent.uab.cat/diego_prior/sites/gent.uab.cat.diego_prior/files/01_a_01_Denyer-Tranfield-Producing-a-Systematic-Review.pdf

Domenico, G., Sit, J., Ishizaka, A. & Nunan, D. (2021). Fake news, social media and marketing: A systematic review. Journal of Business Research, 124, 329-341. https://doi.org/10.1016/j.jbusres.2020.11.037

Doshi-Velez, F. & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. Ml, 1-13. http://arxiv.org/abs/1702.08608

Fishman, J. & Casarett, D. (2006). Mass Media and Medicine: When the Most Trusted Media Mislead. Mayo Clinic Proceedings, 81(3), 291-293. https://doi.org/10.4065/81.3.291

Gilpin, L., Bau, D., Yuan, B., Bajwa, A., Specter, M. & y Kagal, L. (2018). Explaining Explanations: An Overview of Interpretability of Machine Learning. arXiv. http://arxiv.org/abs/1806.00069

Gottfried, J. & Shearer, E. (2016). News Use Across Social Media Platforms 2016. Pew Research Center, http://www.journalism.org/2016/05/26/newsuse-across-social-me dia-platforms-2016/

Gupta, S. & Bashir, L. (2018). Social Networking Usage Questionnaire: Development and Validation in an Indian Higher Education Context. Turkish Online Journal of Distance Education, 19(4), 214-227. https://doi.org/10.17718/tojde.471918

Hou, Z., Du, F., Jiang, H., Zhou, X. & Lin, L. (2020). Assessment of Public Attention, Risk Perception, Emotional and Behavioural Responses to the COVID-19 Outbreak: Social Media Surveillance in China. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3551338

Islam, A., Laato, S., Talukder, S. & y Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159(May), 120201. https://doi.org/10.1016/j.techfore.2020.120201

Laato, S., Islam, A., Farooq, A. & Dhir, A. (2020). Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-response approach. Journal of Retailing and Consumer Services, 57(July), 102224. https://doi.org/10.1016/j.jretconser.2020.102224

Lin, Y., Hu, Z., Alias, H. & Wong, L. P. (2020). Influence of Mass and Social Media on Psychobehavioral Responses Among Medical Students During the Downward Trend of COVID-19 in Fujian, China: Cross-Sectional Study. Journal of Medical Internet Research, 22(7), e19982. https://doi.org/10.2196/19982

Liu, P. (2020). COVID-19 Information Seeking on Digital Media and Preventive Behaviors: The Mediation Role of Worry. Cyberpsychology, Behavior, and Social Networking, 23(10), 677-682. https://doi.org/10.1089/cyber.2020.0250


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