Users’ Perspective on Knowledge Constructs About Covid-19 on Social Media / Perspectiva dos Usuários Sobre a Covid-19 nas Mídias Sociais Media

Cristiane Melchior, Mírian Oliveira

Resumen


Objective: (a) to identify the Social Media Platform (SMP) most used to search for information about the Covid-19 pandemic and its users’ profiles; and (b) to identify the relationship between the constructs of Knowledge Quality (KQ), Knowledge Donation (KD), and Knowledge Collection (KC) in the scope of the Covid-19 pandemic and using a knowledge quality scale and a knowledge donation and collection scale. Design/methodology/approach: Data was collected through a survey with 295 answers. First, we analyze the profile of SMP users through exploratory factor analysis and reliability, which was used to test the hypotheses, followed by multigroup analysis. Findings: Most participants are women between 25 to 34 years with complete post-graduation. The results suggest that the most used SMP to search for or receive information about Covid-19 was Facebook. Most of the respondents identified fake news on SMP but ignored them. The model presents reliability and adequate convergent and discriminant validity. KC represented a medium effect on KQ and KD. The multigroup analysis shows that there is a difference between age groups. Originality: This is the first study to test the relation between the constructs KQ, KC, and KD using data from the Covid-19 pandemic on SMPs.

 

Keywords: Knowledge Quality. Knowledge Donation, Knowledge Collection. Fake News. Social Media Platforms. Social Networks.

 

 

RESUMO

 

Objetivo: (a) identificar a Plataforma de Mídia Social (PMS) mais usada para pesquisar informações sobre a pandemia de Covid-19 e os perfis dos usuários; e (b) identificar a relação entre os construtos de Qualidade do Conhecimento (KQ), Doação de Conhecimento (KD) e Coleta de Conhecimento (KC) no âmbito da pandemia de Covid-19 usando uma escala de qualidade do conhecimento e uma escala de doação e coleta de conhecimento. Metodologia/abordagem: Os dados foram coletados por meio de uma pesquisa com 295 respondentes. Primeiramente, analisamos o perfil dos usuários de PMS por meio de análise fatorial exploratória e de confiabilidade. Também foi realizado teste de hipóteses, seguido de análise multigrupo. Resultados: A maioria dos participantes são mulheres entre 25 e 34 anos com pós-graduação completa. A SMP mais usada para pesquisar ou receber informações sobre a Covid-19 foi o Facebook. A maioria dos entrevistados identificou fake news no Facebook, mas ignorou. O modelo apresentou confiabilidade e validade convergente e discriminante adequadas. O KC representou um efeito médio no KQ e no KD. A análise multigrupo mostrou que há uma diferença entre as faixas etárias. Originalidade: Este é o primeiro estudo a testar a relação entre os construtos KQ, KC e KD usando dados da pandemia de Covid-19 em SMPs.

 

Palavras-chave: Qualidade do Conhecimento. Doação de Conhecimento. Coleta de Conhecimento. Notícias Falsas. Plataformas de Mídia Social. Redes Sociais.


Referencias


ALLCOTT, H.; GENTZKOW, M. Social media and fake news in the 2016 election. Journal of Economic Perspectives, [s. l.], v. 31, n. 2, p. 211–236, 2017.

ALWREIKAT, A. Sharing of Misinformation during COVID-19 Pandemic: Applying the Theory of Planned Behavior with the Integration of Perceived Severity. Science and Technology Libraries, [s. l.], v. 00, n. 00, p. 1–19, 2021.

BAGOZZI, R. P.; YOUJAE YI. On the evaluation of structural equation models. Journal of the academy of marketing science, [s. l.], v. 16, n. 1, p. 74–94, 1988.

BRASIL. Código Civil. Brasília: art. 5o, da Lei n. 10.406/2002, 2002.

CASTRO, C. T. M.; KALLIE, C. S.; SOLAMÃO, S. R. Development and validation of the MNREAD reading acuity chart in Portuguese. Arquivos Brasileiros de Oftalmologia, [s. l.], v. 68, n. 6, p. 777–783, 2005.

COOPER, D. R.; SCHINDLER, P. S.; SUN., J. Business research methods. Vol.9ed. New York: Mcgraw-hill, 2006.

DE VRIES, R. E.; VAN DEN HOOFF, B.; DE RIDDER, J. A. Explaining Knowledge Sharing. Communication Research, [s. l.], v. 33, n. 2, p. 115–135, 2006.

FIGUEIRA, Á.; OLIVEIRA, L. The current state of fake news: challenges and opportunities. Procedia Computer Science, [s. l.], v. 121, p. 817–825, 2017.

GONÇALVES-SÁ, J. In the fight against the new coronavirus outbreak, we must also struggle with human bias. Nature Medicine, [s. l.], v. 26, n. 3, p. 305, 2020. Disponível em: http://dx.doi.org/10.1038/s41591-020-0802-y.

GUERRA, P. H. C. et al. A Measure of Polarization on Social Media Networks Based on Community Boundaries. [S. l.: s. n.], 2013.

HAIR JR, J. F. et al. A primer on partial least squares structural equation modeling (PLS-SEM). [S. l.]: Sage publications, 2014.

HAIRJR., J. F. et al. Multivariate Data Analysis. [s. l.], 2014.

HEMSLEY, J.; MASON, R. M. The nature of knowledge in the social media age: Implications for knowledge management models. Proceedings of the Annual Hawaii International Conference on System Sciences, [s. l.], p. 3928–3937, 2012.

IBGE. Educação - Educa jovens - IBGE - Conheça o Brasil - População - Educação. [S. l.], 2020. Disponível em: https://educa.ibge.gov.br/jovens/conheca-o-brasil/populacao/18317-educacao.html. Acesso em: 6 dez. 2020.

KALIYAR, R. K.; GOSWAMI, A.; NARANG, P. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools and Applications, [s. l.], v. 80, n. 8, p. 11765–11788, 2021.

KEMP, S. Digital 2021: GLOBAL OVERVIEW REPORT. [S. l.], 2021. Disponível em: https://datareportal.com/reports/digital-2021-global-overview-report. .

KOCK, Ned. Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), [s. l.], v. 11, n. 4, p. 1–10, 2015.

KUMAR, A.; BHATIA, M. P. S.; SANGWAN, S. R. Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset. Multimedia Tools and Applications, [s. l.], n. 0123456789, 2021.

LAATO, S. et al. What drives unverified information sharing and cyberchondria during the COVID-19 pandemic?. European Journal of Information Systems, [s. l.], v. 29, n. 3, p. 288–305, 2020.

LAVORGNA, L. et al. Fake news, influencers and health-related professional participation on the Web: A pilot study on a social-network of people with Multiple Sclerosis. Multiple Sclerosis and Related Disorders, [s. l.], v. 25, n. March 2018, p. 175–178, 2018.

LOPEZ, D. M.; BLOBEL, B.; GONZALEZ, C. Information quality in healthcare social media – an architectural approach. Health and Technology, [s. l.], v. 6, n. 1, p. 17–25, 2016.

MANDROLA, J. Response: The necessity of social media literacy. Journal of the American College of Cardiology, [s. l.], v. 65, n. 22, p. 2461, 2015.

MELCHIOR, C.; OLIVEIRA, M. A systematic literature review of the motivations to share fake news on social media platforms and how to fight them. New Media & Society, [s. l.], p. 146144482311742, 2023. Disponível em: http://journals.sagepub.com/doi/10.1177/14614448231174224.

MELCHIOR, C.; OLIVEIRA, M. Health-related fake news on social media platforms: A systematic literature review. New Media & Society, [s. l.], v. 24, n. 6, p. 1500–1522, 2022. Disponível em: http://journals.sagepub.com/doi/10.1177/14614448211038762.

OKPARA, C. V. et al. The moderating role of colour in modelling the effectiveness of COVID-19 YouTube animated cartoons on the health behaviour of social media users in Nigeria. Health Promotion International, [s. l.], v. 36, n. 6, p. 1599–1609, 2021.

SHU, K. et al. Fake news detection: Network data from social media used to predict fakes. CEUR Workshop Proceedings, [s. l.], v. 2041, n. 1, p. 59–66, 2017.

SUI, Y.; ZHANG, B. Determinants of the Perceived Credibility of Rebuttals Concerning Health Misinformation. International Journal of Environmental Research and Public Health, [s. l.], v. 18, n. 3, p. 1345, 2021.

TSAO, S. et al. What social media told us in the time of COVID-19 : a scoping review. The Lancet Digital Health, [s. l.], v. 3, n. 3, p. e175–e194, 2021.

VAN DEN BROUCKE, S. Why health promotion matters to the COVID-19 pandemic, and vice versa. Health Promotion International, [s. l.], v. 35, n. 2, p. 181–186, 2021.

YANG, H. et al. Understanding the motivators affecting doctors’ contributions in online healthcare communities: professional status as a moderator. Behaviour and Information Technology, [s. l.], v. 40, n. 2, p. 146–160, 2021.

YOO, D. K.; VONDEREMBSE, M. A.; RAGU-NATHAN, T. S. Knowledge quality: Antecedents and consequence in project teams. Journal of Knowledge Management, [s. l.], v. 15, n. 2, p. 329–343, 2011.




DOI: http://dx.doi.org/10.12819//2024.21.2.2

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