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

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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