False news around COVID-19 circulated less on Sina Weibo than on Twitter. How to overcome false information?

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https://doi.org/10.17583/rimcis.2020.5386

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Abstract

Since the Coronavirus health emergency was declared, many are the fake news that have circulated around this topic, including rumours, conspiracy theories and myths. According to the World Economic Forum, fake news is one of the threats in today's societies, since this type of information circulates fast and is often inaccurate and misleading. Moreover, fake-news are far more shared than evidence-based news among social media users and thus, this can potentially lead to decisions that do not consider the individual’s best interest. Drawing from this evidence, the present study aims at comparing the type of Tweets and Sina Weibo posts regarding COVID-19 that contain either false or scientific veracious information. To that end 1923 messages from each social media were retrieved, classified and compared. Results show that there is more false news published and shared on Twitter than in Sina Weibo, at the same time science-based evidence is more shared on Twitter than in Weibo but less than false news. This stresses the need to find effective practices to limit the circulation of false information.

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Author Biographies

Cristina Pulido Rodríguez, Universitat Autònoma de Barcleona

Universitat Autònoma de Barcelona

Beatriz Villarejo Carballido, University of Deusto

University of Deusto

Gisela Redondo-Sama, University of Deusto

University of Deusto

Mengna Guo, University of Barcelona

University of Barcelona

Mimar Ramis, University of Barcelona

University of Barcelona

Ramon Flecha, University of Barcelona

University of Barcelona

References

Allcott, H., Gentzkow, M. and Yu, C. (2019) ‘Trends in the diffusion of misinformation on social media’, Research & Politics. SAGE Publications Ltd, 6(2), p. 2053168019848554. doi: 10.1177/2053168019848554.

Google Scholar

Bessi, A. et al. (2015) ‘Science vs conspiracy: collective narratives in the age of misinformation’, PloS one, 10(2), p. e0118093. doi: 10.1371/journal.pone.0118093.

Google Scholar

Bovet, A. and Makse, H. A. (2019) ‘Influence of fake news in Twitter during the 2016 US presidential election’, Nature communications, 10(1), p. 7. doi: 10.1038/s41467-018-07761-2.

Google Scholar

Clement, J. (2020) Leading countries based on number of Twitter users as of January 2020, Statista. Available at: https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/ (Accessed: 20 February 2020).

Google Scholar

Del Vicario, M. et al. (2016) ‘The spreading of misinformation online’, Proceedings of the National Academy of Sciences of the United States of America, 113(3), pp. 554–559. doi: 10.1073/pnas.1517441113.

Google Scholar

Farrell, J., McConnell, K. and Brulle, R. (2019) ‘Evidence-based strategies to combat scientific misinformation’, Nature climate change, 9(3), pp. 191–195. doi: 10.1038/s41558-018-0368-6.

Google Scholar

Fung, I. C.-H. et al. (2016) ‘Social Media’s Initial Reaction to Information and Misinformation on Ebola, August 2014: Facts and Rumors’, Public health reports, 131(3), pp. 461–473. doi: 10.1177/003335491613100312.

Google Scholar

Galarza Molina, R. A. (2019) ‘Networked Gatekeeping and Networked Framing on Twitter Protests in Mexico about the Ayotzinapa Case’, RIMCIS. Hipatia Press, 8(3), pp. 235–266. Available at: https://dialnet.unirioja.es/servlet/articulo?codigo=7216311.

Google Scholar

Giddens, A., Beck, U. and Lash, S. (1994) Reflexive modernization: Politics, tradition and aesthetics in the modern social order. Stanford University Press.

Google Scholar

Howel, L. (2013) Digital wildfires in a hyperconnected world. Global Risks Report. World Economic Forum. Available at: http://www3.weforum.org/docs/WEF_GlobalRisks_Report_2013.pdf.

Google Scholar

Hu, D. et al. (2020) ‘Chinese social media suggest decreased vaccine acceptance in China: An observational study on Weibo following the 2018 Changchun Changsheng vaccine incident’, Vaccine, 38(13), pp. 2764–2770. doi: 10.1016/j.vaccine.2020.02.027.

Google Scholar

Lazer, D. M. J. et al. (2018) ‘The science of fake news’, Science, 359(6380), pp. 1094–1096. doi: 10.1126/science.aao2998.

Google Scholar

Lewandowsky, S. et al. (2012) ‘Misinformation and Its Correction: Continued Influence and Successful Debiasing’, Psychological science in the public interest: a journal of the American Psychological Society, 13(3), pp. 106–131. doi: 10.1177/1529100612451018.

Google Scholar

Merino, J. G. (2014) ‘Response to Ebola in the US: misinformation, fear, and new opportunities’, BMJ , 349, p. g6712. doi: 10.1136/bmj.g6712.

Google Scholar

Pulido, C. et al. (in press) ‘CoVid-19 infodemic: More retweets for science-based information on coronavirus than for false-information’, International sociology: journal of the International Sociological Association.

Google Scholar

Redondo-Sama, G. et al. (2020) ‘Impact Assessment in Psychological Research and Communicative Methodology’, Frontiers in psychology. Frontiers, 11, p. 286.

Google Scholar

Scheufele, D. A. and Krause, N. M. (2019) ‘Science audiences, misinformation, and fake news’, Proceedings of the National Academy of Sciences of the United States of America, 116(16), pp. 7662–7669. doi: 10.1073/pnas.1805871115.

Google Scholar

Shu, K. et al. (2017) ‘Fake News Detection on Social Media’, ACM SIGKDD Explorations Newsletter. ACM PUB27 New York, NY, USA, 19(1), pp. 22–36. Available at: https://arxiv.org/pdf/1708.01967.pdf (Accessed: 20 February 2020).

Google Scholar

Statista Research Department (2019) China: number of Sina Weibo users 2017-2021, Statista. Available at: https://www.statista.com/statistics/941456/china-number-of-sina-weibo-users/ (Accessed: 21 February 2020).

Google Scholar

Vosoughi, S., Roy, D. and Aral, S. (2018) ‘The spread of true and false news online’, Science, 359(6380), pp. 1146–1151. doi: 10.1126/science.aap9559.

Google Scholar

Wikipedia contributors (2020) Censorship of Twitter, Wikipedia, The Free Encyclopedia. Available at: https://en.wikipedia.org/w/index.php?title=Censorship_of_Twitter&oldid=938453563 (Accessed: 20 February 2020).

Google Scholar

World Health Organization (2020a) Coronavirus disease 2019 (COVID-19) Situation Report – 47. Available at: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200307-sitrep-47-covid-19.pdf?sfvrsn=27c364a4_4. (Accessed: 20 February 2020).

Google Scholar

World Health Organization (2020b) Novel Coronavirus(2019-nCoV). Situation Report - 18. Available at: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200207-sitrep-18-ncov.pdf?sfvrsn=fa644293_2. (Accessed: 20 February 2020).

Google Scholar

Zhu, T. et al. (2013) ‘The velocity of censorship: High-fidelity detection of microblog post deletions’, in 22nd USENIX Security Symposium. 22nd Security Symposium, USENIX, pp. 227–240.

Google Scholar

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2020-07-30

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How to Cite

Pulido Rodríguez, C., Villarejo Carballido, B., Redondo-Sama, G., Guo, M., Ramis, M., & Flecha, R. (2020). False news around COVID-19 circulated less on Sina Weibo than on Twitter. How to overcome false information?. International and Multidisciplinary Journal of Social Sciences, 9(2), 107–128. https://doi.org/10.17583/rimcis.2020.5386