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>>欢迎咨询报考2027年硕士/博士研究生<<        张新生(1978~),男,博士,教授(博导),管理学院副院长。2009年12月毕业于西安电子科技大学,获得博士学位。2010年10月晋升为副教授,佛罗里达大学访问学者(2013-2014),2016年12月晋升为教授,现在西安建筑科技大学管理学院从事教学和科研工作。近年来主持国家自然科学基金1项、国家社科基金后期资助项目1项,教育部人文社科规划项目1项,陕西省重点产业链项目1项,陕西省自然科学基金3项、陕西省社科基金2项、陕西省教育厅自然科学基金3项等,主持横向项目6项,并参与了多项课题的研究工作。主要研究方向包括:智能社会治理;管理智能决策与优化;能资环(能源、资源、环境)智能管理与优化...
zhangxinsheng
Professor
Paper Publications
Dynamic modelling and analysis of online rumour propagation considering dual debunking mechanisms and time delay effects
Release time:2026-05-25 Hits:
Impact Factor:
3.6
Journal:
Humanities and Social Sciences Communications
Funded by:
国家社科基金
Key Words:
Rumour propagation; Time delay; Multimodal propagation characteristics; Complex network; Agent-based modelling
Abstract:
With the widespread adoption of online social platforms, multimodal rumours integrating text, images, and audio have become increasingly prevalent, posing severe threats to public opinion and social stability due to their complex propagation dynamics and resistance to debunking. Existing rumour propagation models rarely examine the synergistic interaction between individual and official debunking, nor do they account for the differentiated effects of official reports across user states. To address these limitations, this paper proposes a novel SIOUR rumour propagation model that integrates dual debunking mechanisms, differentiated state transitions, three time-delay factors, and multimodal propagation characteristics. Theoretical analysis derives the basic reproduction number and establishes the local and global stability of the rumour-free equilibrium for both delayed and non-delayed systems. Numerical simulations investigate the influence of key parameters, time delays, multimodal features, and network topologies. By combining mean-field analysis with agent-based modelling, we evaluate the model under heterogeneous network structures and identify the limitations of homogeneous approximations. Finally, comparative experiments on four public rumour events show that the proposed model significantly outperforms existing approaches across multiple evaluation metrics.
Indexed by:
Journal paper
Discipline:
Management Science
Document Type:
J
ISSN No.:
2662-9992
Translation or Not:
no
Date of Publication:
2026-01-01
Included Journals:
SSCI
Links to published journals:
https://doi.org/10.1057/s41599-026-07243-7

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