The COVID-19 social media infodemic

Abstract

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number $$R_0$$ R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.

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References 52

1. 1.

   Organization, W. H. Naming the coronavirus disease (COVID-19) and the virus that causes it. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it (2020 (accessed April 9, 2020)).

   Not in our library

2. 2.

   Quattrociocchi, W. Part 2-social and political challenges: 2.1 western democracy in crisis? In Global Risk Report World Economic Forum (2017).

   Not in our library

3. 3.

   WHO Situation Report 13. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200202-sitrep-13-ncov-v3.pdf?sfvrsn=195f4010_6. Accessed: 2010-09-30.

   Not in our library

4. 4.

   Zarocostas J. How to fight an infodemic. Lancet. 2020;395:676.

   Not in our library10.1016/s0140-6736(20)30461-x

5. 5.

   Organization, W. H. Director-general’s remarks at the media briefing on 2019 novel Coronavirus on 8 February 2020. https://www.who.int/dg/speeches/detail/director-general-s-remarks-at-the-media-briefing-on-2019-novel-coronavirus---8-february-2020 (2020 (accessed April 9, 2020)).

   Not in our library

6. 6.

   Mendoza, M., Poblete, B. & Castillo, C. Twitter under crisis: Can we trust what we RT?. Proceedings of the first workshop on social media analytics71–79 (2010).

   Not in our library

7. 7.

   Starbird, K., Maddock, J., Orand, M., Achterman, P. & Mason, R. M. Rumors, false flags, and digital vigilantes: Misinformation on twitter after the 2013 boston marathon bombing. IConference 2014 Proceedings (2014).

   Not in our library

8. 8.

   Kim L, Fast SM, Markuzon N. Incorporating media data into a model of infectious disease transmission. PLoS ONE. 2019;14:1.

   Not in our library10.1371/journal.pone.0197646

9. 9.

   John, T. & Ben Wedeman, C. Italy prohibits travel and cancels all public events in its northern region to contain Coronavirus. https://edition.cnn.com/2020/03/08/europe/italy-coronavirus-lockdown-europe-intl/index.html (2020 (accessed April 9, 2020)).

   Not in our library

10. 10.

    Sharot T, Sunstein CR. How people decide what they want to know. Nat. Hum. Behav. 2020;2020:1-6.

    Not in our library10.1038/s41562-019-0793-1

11. 11.

    Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M. Real-time influenza forecasts during the 2012–2013 season. Nat. Commun. 2013;4:1-10.

    Not in our library10.1038/ncomms3837

12. 12.

    Viboud C, Vespignani A. The future of influenza forecasts. Proc. Natl. Acad. Sci. 2019;116:2802-2804.

    Not in our library10.1073/pnas.1822167116

13. 13.

    Kulshrestha, J. et al. Quantifying search bias: Investigating sources of bias for political searches in social media. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 417–432 (2017).

    Not in our library

14. 14.

    Schmidt AL, et al. Anatomy of news consumption on Facebook. Proc. Natl. Acad. Sci. 2017;114:3035-3039.

    Not in our library10.1073/pnas.1617052114

15. 15.

    Starnini M, Frasca M, Baronchelli A. Emergence of metapopulations and echo chambers in mobile agents. Sci. Rep. 2016;6:31834.

    Not in our library10.1038/srep31834

16. 16.

    Schmidt AL, Zollo F, Scala A, Betsch C, Quattrociocchi W. Polarization of the vaccination debate on Facebook. Vaccine. 2018;36:3606-3612.

    Not in our library10.1016/j.vaccine.2018.05.040

17. 17.

    Del Vicario M, et al. The spreading of misinformation online. Proc. Natl. Acad. Sci. 2016;113:554-559.

    Not in our library10.1073/pnas.1517441113

18. 18.

    Bessi A, et al. Science vs. conspiracy: collective narratives in the age of misinformation. PLoS ONE. 2015;10:e0118093.

    Not in our library10.1371/journal.pone.0118093

19. 19.

    Cinelli M, et al. Selective exposure shapes the Facebook news diet. PLoS ONE. 2020;15:e0229129.

    Not in our library10.1371/journal.pone.0229129

20. 20.

    Zollo F, et al. Debunking in a world of tribes. PLoS ONE. 2017;12:1.

    Not in our library10.1371/journal.pone.0181821

21. 21.

    Baronchelli A. The emergence of consensus: a primer. R. Soc. Open Sci. 2018;5:172189.

    Not in our library10.1098/rsos.172189

22. 22.
    Echo Chambers: Emotional Contagion and Group Polarization on Facebook · 2016

    No notice
23. 23.

    Bail CA, et al. Exposure to opposing views on social media can increase political polarization. Proc. Natl. Acad. Sci. 2018;115:9216-9221.

    Not in our library10.1073/pnas.1804840115

24. 24.

    Vicario MD, Quattrociocchi W, Scala A, Zollo F. Polarization and fake news: early warning of potential misinformation targets. ACM Trans. Web (TWEB). 2019;13:1-22.

    Not in our library10.1145/3316809

25. 25.

    Wardle, C. & Derakhshan, H. Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe report27 (2017).

    Not in our library

26. 26.

    Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018;359:1146-1151.

    Not in our library10.1126/science.aap9559

27. 27.

    Ruths D. The misinformation machine. Science. 2019;363:348-348.

    Not in our library10.1126/science.aaw1315

28. 28.

    Schild, L. et al. “ go eat a bat, chang!”: An early look on the emergence of sinophobic behavior on web communities in the face of covid-19. arXiv preprintarXiv:2004.04046 (2020).

    Not in our library

29. 29.

    Velásquez, N. et al.Hate multiverse spreads malicious COVID-19 content online beyond individual platform control. Preprint arXiv:2004.00673 (2020).

    Not in our library

30. 30.

    Ferrara, E. What types of COVID-19 conspiracies are populated by twitter bots? First Monday (2020).

    Not in our library

31. 31.

    Alam, F. et al.Fighting the COVID-19 infodemic: modeling the perspective of journalists, fact-checkers, social media platforms, policy makers, and the society. Preprint arXiv:2005.00033 (2020).

    Not in our library

32. 32.

    Shahi, G. K., Dirkson, A. & Majchrzak, T. A. An exploratory study of COVID-19 misinformation on twitter. Preprint arXiv:2005.05710 (2020).

    Not in our library10.1016/j.osnem.2020.100104

33. 33.
    Influence of fake news in Twitter during the 2016 US presidential election · 2019

    No notice
34. 34.

    (MBFC), M. B. C. Media bias/fact check, the most comprehensive Meida bias check resource. https://mediabiasfactcheck.com/ (2020 (accessed April 9, 2020)).

    Not in our library

35. 35.

    Romero, D. M., Meeder, B. & Kleinberg, J. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, 695–704 (2011).

    Not in our library

36. 36.

    Organization, W. H. Novel Coronavirus (2019-NCOV) situation report-1 21 January 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4 (2020 (accessed April 9, 2020)).

    Not in our library

37. 37.

    Pellis L, et al. Eight challenges for network epidemic models. Epidemics. 2015;10:58-62.

    Not in our library10.1016/j.epidem.2014.07.003

38. 38.

    Liu Y, et al. Characterizing super-spreading in microblog: an epidemic-based information propagation model. Physica A. 2016;463:202-218.

    Not in our library10.1016/j.physa.2016.07.022

39. 39.

    Skaza J, Blais B. Modeling the infectiousness of twitter hashtags. Physica A. 2017;465:289-296.

    Not in our library10.1016/j.physa.2016.08.038

40. 40.

    Davis JT, Perra N, Zhang Q, Moreno Y, Vespignani A. Phase transitions in information spreading on structured populations. Nat. Phys. 2020;2020:1-7.

    Not in our library

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