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Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies. / Alsulami, Amer; Glukhov, Anton; Shishlenin, Maxim и др.

в: Scientific Reports, Том 12, № 1, 20447, 12.2022.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Alsulami, A, Glukhov, A, Shishlenin, M & Petrovskii, S 2022, 'Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies', Scientific Reports, Том. 12, № 1, 20447. https://doi.org/10.1038/s41598-022-23917-z

APA

Alsulami, A., Glukhov, A., Shishlenin, M., & Petrovskii, S. (2022). Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies. Scientific Reports, 12(1), [20447]. https://doi.org/10.1038/s41598-022-23917-z

Vancouver

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 дек.;12(1):20447. doi: 10.1038/s41598-022-23917-z

Author

Alsulami, Amer ; Glukhov, Anton ; Shishlenin, Maxim и др. / Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies. в: Scientific Reports. 2022 ; Том 12, № 1.

BibTeX

@article{fa5306821c16435c8b3fec16584ce4be,
title = "Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies",
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.",
keywords = "Movement, Biotin, France, Russia",
author = "Amer Alsulami and Anton Glukhov and Maxim Shishlenin and Sergei Petrovskii",
note = "Funding Information: A.G. and M.S. was supported by the Mathematical Center in Akademgorodok (Novosibirsk), the agreement with Ministry of Science and High Education of the Russian Federation number 075-15-2019-1675. S.P. was supported by the RUDN University Strategic Academic Leadership Program. A.A. expresses his gratitude to the University of Leicester for the comprehensive academic support provided during his PhD term (under supervision by S.P.). Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = dec,
doi = "10.1038/s41598-022-23917-z",
language = "English",
volume = "12",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies

AU - Alsulami, Amer

AU - Glukhov, Anton

AU - Shishlenin, Maxim

AU - Petrovskii, Sergei

N1 - Funding Information: A.G. and M.S. was supported by the Mathematical Center in Akademgorodok (Novosibirsk), the agreement with Ministry of Science and High Education of the Russian Federation number 075-15-2019-1675. S.P. was supported by the RUDN University Strategic Academic Leadership Program. A.A. expresses his gratitude to the University of Leicester for the comprehensive academic support provided during his PhD term (under supervision by S.P.). Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

KW - Movement

KW - Biotin

KW - France

KW - Russia

UR - http://www.scopus.com/inward/record.url?scp=85142896669&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b4bf6579-c4d7-3ae9-bdae-791323b83bbf/

U2 - 10.1038/s41598-022-23917-z

DO - 10.1038/s41598-022-23917-z

M3 - Article

C2 - 36443352

AN - SCOPUS:85142896669

VL - 12

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 20447

ER -

ID: 40095480