IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0076027.html
   My bibliography  Save this article

Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network

Author

Listed:
  • Peng Bao
  • Hua-Wei Shen
  • Wei Chen
  • Xue-Qi Cheng

Abstract

Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.

Suggested Citation

  • Peng Bao & Hua-Wei Shen & Wei Chen & Xue-Qi Cheng, 2013. "Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-7, October.
    • Handle: RePEc:plo:pone00:0076027
    DOI: 10.1371/journal.pone.0076027
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0076027
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0076027&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0076027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ding, Qin & Li, Weihua & Hu, Xiangming & Zheng, Zhiming & Tang, Shaoting, 2020. "The SIS diffusion process in complex networks with independent spreaders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    2. Zhu, He & Ma, Jing, 2019. "Analysis of SHIR rumor propagation in random heterogeneous networks with dynamic friendships," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 257-271.
    3. Peng, Jingsi & Min, Shi, 2021. "Does Internet Use Improve Rural Residents' Behavior of Food Safety?," 2021 Conference, August 17-31, 2021, Virtual 315238, International Association of Agricultural Economists.
    4. Soumajyoti Sarkar & Paulo Shakarian & Danielle Sanchez & Mika Armenta & Kiran Lakkaraju, 2020. "Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-37, July.
    5. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & Zhong, Chen-Yang & Qiu, Tian & Shi, Yong-Dong & Wang, Li-Liang, 2016. "A generalized voter model with time-decaying memory on a multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 95-105.
    6. Yang, Dingda & Liao, Xiangwen & Shen, Huawei & Cheng, Xueqi & Chen, Guolong, 2018. "Modeling the reemergence of information diffusion in social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1493-1500.
    7. Liu, Liang & Qu, Bo & Chen, Bin & Hanjalic, Alan & Wang, Huijuan, 2018. "Modelling of information diffusion on social networks with applications to WeChat," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 318-329.
    8. Peng Bao & Hua-Wei Shen & Junming Huang & Haiqiang Chen, 2018. "Mention effect in information diffusion on a micro-blogging network," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-13, March.
    9. Liu, Nairong & An, Haizhong & Gao, Xiangyun & Li, Huajiao & Hao, Xiaoqing, 2016. "Breaking news dissemination in the media via propagation behavior based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 44-54.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0076027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.