False news around COVID-19 circulated less on Sina Weibo than on Twitter. How to overcome false information?
Keywords:
Downloads
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.
Downloads
Metrics
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 ScholarBessi, 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 ScholarBovet, 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 ScholarClement, 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 ScholarDel 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 ScholarFarrell, 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 ScholarFung, 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 ScholarGalarza 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 ScholarGiddens, A., Beck, U. and Lash, S. (1994) Reflexive modernization: Politics, tradition and aesthetics in the modern social order. Stanford University Press.
Google ScholarHowel, 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 ScholarHu, 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 ScholarLazer, D. M. J. et al. (2018) ‘The science of fake news’, Science, 359(6380), pp. 1094–1096. doi: 10.1126/science.aao2998.
Google ScholarLewandowsky, 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 ScholarMerino, J. G. (2014) ‘Response to Ebola in the US: misinformation, fear, and new opportunities’, BMJ , 349, p. g6712. doi: 10.1136/bmj.g6712.
Google ScholarPulido, 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 ScholarRedondo-Sama, G. et al. (2020) ‘Impact Assessment in Psychological Research and Communicative Methodology’, Frontiers in psychology. Frontiers, 11, p. 286.
Google ScholarScheufele, 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 ScholarShu, 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 ScholarStatista 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 ScholarVosoughi, 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 ScholarWikipedia 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 ScholarWorld 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 ScholarWorld 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 ScholarZhu, 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 ScholarDownloads
Published
Almetric
Dimensions
How to Cite
Issue
Section
License
Open Access Policy: this work is licensed under a Creative Commons CC BY License.
All the manuscripts should be available through the Electronic platform where the Journal is based (Open Journal System).
Since its foundation in 2012, RIMCIS has not charged Article Processing Charges (APCs) to the authors for the publication of their manuscripts. Despite the voluntary work that is carried out, the expenses associated with both the publication process and the necessary services for publication are not covered. From June 19th 2021, these processing charges should be covered by the authors once the articles are accepted. Thus, authors will pay 600 euros to cover these charges (plus 21% VAT only applicable to payments from institutions, companies, individuals and professionals residing in Spain; individuals residing in the EU, and companies, organizations and professionals residing in the EU without VAT Number).
RIMCIS does not have article's submission charge.