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Research Article | Open Access

Dynamic analysis of a rumor propagation model considering individual identification ability

Xintong Wang1Sida Kang2Yuhan Hu1( )
School of Science, University of Science and Technology Liaoning, Anshan, 114051, China
School of Business Administration, University of Science and Technology Liaoning, Anshan 114051, China
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Abstract

In the 2016 US presidential election, the widespread propagation of false news on social media, especially rumors against Hillary Clinton, quickly affected the emotions and decisions of some voters, highlighting the potential impact of rumors on voters' perceptions and election results. An individual's ability to identify rumor information determines their tendency to judge and spread rumors, thus directly affecting the spread of rumors and society's trust in information. Considering the impact of individual identification ability on rumor propagation, this paper established a new rumor propagation model, calculated the basic reproductive number of the model, and proved the existence of equilibrium points in the model as well as their local and global asymptotic stability. Meanwhile, based on Pontryagin's maximum principle, we chose the probability of contact between ignorant individuals and rumor spreaders, the probability of conversion of spreaders into immunized individuals, and the contact rate between rumor spreaders and truth spreaders as the optimal control variables, and obtained an effective strategy for reducing rumor spreading. The numerical simulation verified the results of theoretical analysis. The findings of this study suggest that enhancing individual identification ability can effectively slow down the propagation of rumors.

CLC number: 34D20, 37D35, 49J15

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AIMS Mathematics
Pages 2295-2320

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Cite this article:
Wang X, Kang S, Hu Y. Dynamic analysis of a rumor propagation model considering individual identification ability. AIMS Mathematics, 2025, 10(2): 2295-2320. https://doi.org/10.3934/math.2025107

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Received: 04 December 2024
Revised: 20 January 2025
Accepted: 26 January 2025
Published: 15 February 2025
©2025 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)