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Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies Full article

Journal Scientific Reports
ISSN: 2045-2322
Output data Year: 2022, Volume: 12, Number: 1, DOI: 10.1038/s41598-022-23917-z
Authors Alsulami Amer 1 , Glukhov Anton 2 , Shishlenin Maxim 2,3 , Petrovskii Sergei 1,4
Affiliations
1 School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
2 Novosibirsk State University, Novosibirsk, Russia
3 Sobolev Institute of Mathematics, Novosibirsk, Russia
4 Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, Russia

Funding (1)

1 Mathematical Center in Akademgorodok 075-15-2019-1675

Abstract: Social protests, in particular in the form of street protests, are a frequent phenomenon of modern world often making a significant disruptive effect on the society. Understanding the factors that can affect their duration and intensity is therefore an important problem. In this paper, we consider a mathematical model of protests dynamics describing how the number of protesters change with time. We apply the model to two events such as the Yellow Vest Movement 2018–2019 in France and Khabarovsk protests 2019–2020 in Russia. We show that in both cases our model provides a good description of the protests dynamics. We consider how the model parameters can be estimated by solving the inverse problem based on the available data on protesters number at different time. The analysis of parameter sensitivity then allows for determining which factor(s) may have the strongest effect on the protests dynamics.
Cite: Alsulami A. , Glukhov A. , Shishlenin M. , Petrovskii S.
Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies
Scientific Reports. 2022. V.12. N1. DOI: 10.1038/s41598-022-23917-z WOS Scopus РИНЦ OpenAlex
Dates:
Submitted: May 18, 2022
Accepted: Nov 7, 2022
Published online: Nov 26, 2022
Identifiers:
Web of science: WOS:000889945500001
Scopus: 2-s2.0-85142896669
Elibrary: 59826497
OpenAlex: W4310197336
Citing:
DB Citing
Scopus 5
Web of science 3
OpenAlex 6
Elibrary 3
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