Modeling the transmission dynamics of racism propagation with community resilience.

Q1 Mathematics

Computational Social Networks Pub Date : 2021-01-01 Epub Date: 2021-11-06 DOI:10.1186/s40649-021-00102-2

Dejen Ketema Mamo

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引用次数: 0

Abstract

Racism spreading can have a vital influence on people's lives, declining adherence, pretending political views, and recruiters' socio-economical crisis. Besides, Web 2.0 technologies have democratized the creation and propagation of racist information, which facilitated the rapid spreading of racist messages. In this research work, the impact of community resilience on the spread dynamics of racism was assessed. To investigate the effect of resilience-building, new SERDC mathematical model was formulated and analyzed. The racism spread is under control where $R_{0} < 1$ , whereas persist in the community whenever $R_{0} > 1$ . Sensitivity analysis of the parameters value of the model are conducted. The rising of transmission and racial extremeness rate provides the prevalence of racism spread. Effective community resilience decline the damages, mitigate, and eradicate racism propagation. Theoretical analysis of the model are backed up by numerical results. Despite the evidence of numerical simulations, reducing the transmission and racial extremeness rate by improving social bonds and solidarity through community resilience could control the spread of racism.

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建立具有社区复原力的种族主义传播动态模型。

种族主义的传播会对人们的生活、依附性的下降、政治观点的伪装以及招募者的社会经济危机产生至关重要的影响。此外，Web 2.0 技术使种族主义信息的创造和传播民主化，这为种族主义信息的快速传播提供了便利。在这项研究工作中，我们评估了社区复原力对种族主义传播动态的影响。为了研究复原力建设的影响，我们建立并分析了新的 SERDC 数学模型。当 R 0 1 时，种族主义的传播受到控制，而当 R 0 > 1 时，种族主义在社区中持续存在。对模型的参数值进行了敏感性分析。传播率和种族极端化率的上升提供了种族主义传播的普遍性。有效的社区复原力可以降低种族主义传播造成的损害，减轻并根除种族主义传播。模型的理论分析得到了数值结果的支持。尽管有数值模拟的证据，但通过社区复原力改善社会纽带和团结来降低传播率和种族极端化率，可以控制种族主义的传播。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.