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An extended SEIR model considering homepage effect for the information propagation of online social networks

Author

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  • Zhao, Danling
  • Sun, Jianbin
  • Tan, Yuejin
  • Wu, Jianhong
  • Dou, Yajie

Abstract

In this work we extend the SEIR model as in epidemic disease modeling to investigate the propagation dynamics of the information online. Here, we go one step further and takes the homepage effect into consideration, acting as the infectious sources in infectious diseases. Aside of the mathematical analysis, the results of simulation also show some managerial insights that are helpful for controlling the information spread. Furthermore, a case study, usingthe information propagation data in Digg.com, is carried out to examine the effectiveness of two propagation models. Our study could be a starting point for developing a more realistic model about the online propagation dynamics of multiple messages.

Suggested Citation

  • Zhao, Danling & Sun, Jianbin & Tan, Yuejin & Wu, Jianhong & Dou, Yajie, 2018. "An extended SEIR model considering homepage effect for the information propagation of online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1019-1031.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:1019-1031
    DOI: 10.1016/j.physa.2018.08.006
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    References listed on IDEAS

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    1. Xue Zhou & Yong Hu & Yue Wu & Xi Xiong, 2015. "Influence analysis of information erupted on social networks based onSIRmodel," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(02), pages 1-11.
    2. Qiang Yan & Lianren Wu & Chao Liu & Xiye Li, 2013. "Information Propagation in Online Social Network Based on Human Dynamics," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-6, May.
    3. Xu, Rui & Wang, Zhili & Zhang, Fengqin, 2015. "Global stability and Hopf bifurcations of an SEIR epidemiological model with logistic growth and time delay," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 332-342.
    4. Freeman, Mark & McVittie, James & Sivak, Iryna & Wu, Jianhong, 2014. "Viral information propagation in the Digg online social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 87-94.
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    Cited by:

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    2. Shou, Ming-Huan & Wang, Zheng-Xin & Lou, Wen-Qian, 2021. "Effect evaluation of non-pharmaceutical interventions taken in China to contain the COVID-19 epidemic based on the susceptible-exposed-infected-recovered model," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. An, Xuming & Ding, Li & Hu, Ping, 2020. "Information propagation with individual attention-decay effect on activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    4. Xinhai Lu & Yanwei Zhang & Handong Tang, 2021. "Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China," Land, MDPI, vol. 10(2), pages 1-21, February.
    5. Juyoung Song & Dal-Lae Jin & Tae Min Song & Sang Ho Lee, 2023. "Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data," IJERPH, MDPI, vol. 20(9), pages 1-11, May.
    6. S. P. Zemtsov & V. L. Baburin, 2020. "COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions," Regional Research of Russia, Springer, vol. 10(3), pages 273-290, July.
    7. Zhang, Mingli & Qin, Simeng & Zhu, Xiaoxia, 2021. "Information diffusion under public crisis in BA scale-free network based on SEIR model — Taking COVID-19 as an example," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    8. Yin, Fulian & Xia, Xinyu & Zhang, Xiaojian & Zhang, Mingjia & Lv, Jiahui & Wu, Jianhong, 2021. "Modelling the dynamic emotional information propagation and guiding the public sentiment in the Chinese Sina-microblog," Applied Mathematics and Computation, Elsevier, vol. 396(C).

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