Your shopping cart is empty.
Log in

Politicization of discussions of Covid-19 on Twitter

I.G. Ovchinnikova, L.M. Ermakova, D.M. Nurbakova
$2.50

UDC 81`27

https://doi.org/10.20339/PhS.6-21.003

 

Ovchinnikova Irina G.,

Doctor of Philology, Professor,

Institute of Linguistic and Intercultural Communication

Sechenov First Moscow State Medical University

e-mail: ovchinnikova.ig@1msmu.ru

Ermakova Liana M.,

Candidate of Physics and Mathematics Sciences, Associate Professor

of the Laboratory of Digital Humanities

University of Western Brittany (Brest, France)

e-mail: liana.ermakova@univ-brest.fr

Nurbakova Diana M.,

Candidate of Physics and Mathematics Sciences,

Associate Professor of the Computer Science Department

National Institute of Applied Sciences (Lyon, France)

e-mail: diana.nurbakova@insa-lyon.fr

 

Power of social media including Twitter for English speaking community to shape public opinion becomes critical during the current pandemic because of misinformation. The existing studies on spreading misinformation on social media hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message about the Covid-19 treatment is quoted or receives a reply. Public persons discuss medical information on Twitter providing fast and simple response to complex medical problems that users find very attractive to follow. Followers generate information cascades while quoting and commenting on the initial message. In the cascades, medical information from the initial tweet is often distorted. The discussion of the Covid-19 treatment in the cascades is politicized according to users’ political sympathies. We show a significant information shift in cascades initiated by public figures during the Covid-19 pandemic. The study provide valuable insights for the semantic analysis of information distortion.

Keywords: social media, Covid-19, information cascade, public figures, semantic shifts, misinformation.

 

References

1.         Ovchinnikova I.G. Kommunikatsiia i identifikatsiia v sotsial’nykh setiakh: faktory, tipazhi, natsional’no-kul’turnaia spetsifika (na materiale sotsial’noi seti Tvitter) // Vestnik Permskogo universiteta. Seriia: Politologiia. 2013. No. 2 (22). S. 143–156.

2.         Why we retweet scale / A. Majmundar, J.P. Allem, T. Boley Cruz, J.B. Unger // PloS one. 2018. Vol. 13 (10). P. 1–12. DOI: org/10.1371/journal.pone.0206076.

3.         Prediction of retweet cascade size over time / A. Kupavskii, L. Ostroumova, A. Umnov et al. // Proceedings of the 21st ACM International Conference on Information and Knowledge Management (Maui Hawaii USA), 2018. P. 2335–2338.‏

4.         Boyd D., Golder S., Lotan G. Tweet, Tweet, Retweet: Conversational aspects of retweeting on Twitter // 43rd Hawaii International Conference on System Sciences. 2010. P. 1–10.

5.         Puti rossiiskoi infodemii: ot WhatsApp do Sledstvennogo komiteta / A.S. Arkhipova, D.A. Radchenko, I.V. Kozlova i dr. // Monitoring obshchestvennogo mneniia: ekonomicheskie i sotsial’nye peremeny. 2020. No. 6 (160). S. 231–265.‏

6.         Types, sources, and claims of COVID-19 misinformation / J.S. Brennen, F.M. Simon, P.N. Howard, R.K. Nielsen // Reuters Institute. 2020. Vol. 7. 13 p. https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covi....

7.         Analysing how people orient to and spread rumours in social media by looking at conversational threads / A. Zubiaga, M. Liakata, R. Procter et al. // PloS one. 2016. No. 11 (3). P. 1–29. DOI:10.1371/journal.pone.0150989.

8.         Ribeiro M.H., Gligoric K., West R. Message distortion in information cascades // Proceedings of the 2019 World Wide Web Conference (WWW ‘19), May 13–17, 2019, San Francisco, CA, USA. 2019. P. 1–12. DOI: org/10.1145/3308558.3313531.

9.         Outtweeting the twitterers — predicting information cascades in microblogs / W. Galuba, K. Aberer, D. Chakraborty et al. // Proceedings of the 3rd Workshop on Online Social Networks (WOSN 2010, Boston, Massachusetts, USA). 2010. No. 10. P. 3–11.‏

10.       Exploring the collective human behaviour in cascading systems: a comprehensive framework / Y. Lu, L. Yu, T. Zhang et al. // Knowledge and Information Systems. 2020. No. 62 (12). P. 4599–4623.‏

11.       Pennycook G., Rand D.G. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning // Cognition. 2019. No. 188. P. 39–50.‏

12.       Ermakova L., Nurbakova D., Ovchinnikova I. Covid or not Covid? Topic shift in information cascades on twitter // Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM, Barcelona, Spain). 2020. P. 32–37.‏

13.       Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention / G. Pennycook, J. McPhetres, Y. Zhang et al. // Psychological Science. 2020. No. 31 (7). P. 681–692.

14.       Potapova R.K., Komalova L.R. Polnotekstovaia annotirovannaia baza dannykh russkoiazychnykh polilogov sotsial’no-setevogo diskursa: podkorpus “politicheskie transformatsii” // Vestnik Moskovskogo gosudarstvennogo lingvisticheskogo universiteta. Gumanitarnye nauki. 2018. No. 6 (797). S. 84–98.‏