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Detecting and modelling real percolation and phase transitions of information on social media

Author

Listed:
  • Jiarong Xie

    (Sun Yat-sen University)

  • Fanhui Meng

    (Sun Yat-sen University)

  • Jiachen Sun

    (Sun Yat-sen University)

  • Xiao Ma

    (Sun Yat-sen University)

  • Gang Yan

    (Tongji University
    Tongji University)

  • Yanqing Hu

    (Sun Yat-sen University
    Southern Marine Science and Engineering Guangdong Laboratory)

Abstract

It is widely believed that information spread on social media is a percolation process, with parallels to phase transitions in theoretical physics. However, evidence for this hypothesis is limited, as phase transitions have not been directly observed in any social media. Here, through an analysis of 100 million Weibo and 40 million Twitter users, we identify percolation-like spread and find that it happens more readily than current theoretical models would predict. The lower percolation threshold can be explained by the existence of positive feedback in the coevolution between network structure and user activity level, such that more-active users gain more followers. Moreover, this coevolution induces an extreme imbalance in users’ influence. Our findings indicate that the ability of information to spread across social networks is higher than expected, with implications for many information-spread problems.

Suggested Citation

  • Jiarong Xie & Fanhui Meng & Jiachen Sun & Xiao Ma & Gang Yan & Yanqing Hu, 2021. "Detecting and modelling real percolation and phase transitions of information on social media," Nature Human Behaviour, Nature, vol. 5(9), pages 1161-1168, September.
    • Handle: RePEc:nat:nathum:v:5:y:2021:i:9:d:10.1038_s41562-021-01090-z
    DOI: 10.1038/s41562-021-01090-z
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    Cited by:

    1. Liang, Yuan & Qi, Mingze & Huangpeng, Qizi & Duan, Xiaojun, 2023. "Percolation of interlayer feature-correlated multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Omar A. Alismaiel & Javier Cifuentes-Faura & Waleed Mugahed Al-Rahmi, 2022. "Online Learning, Mobile Learning, and Social Media Technologies: An Empirical Study on Constructivism Theory during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    3. Junya Wang & Yi-Jiao Zhang & Cong Xu & Jiaze Li & Jiachen Sun & Jiarong Xie & Ling Feng & Tianshou Zhou & Yanqing Hu, 2024. "Reconstructing the evolution history of networked complex systems," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Ai, Jun & He, Tao & Su, Zhan, 2023. "Identifying influential nodes in complex networks based on resource allocation similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    5. Sun, Jiachen & Feng, Ling & Du, Mingwei & Ma, Xiao & Fan, Zhengping & Gloor, Peter & Hu, Yanqing, 2021. "Ultra-efficient information detection on large-scale online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    6. Fanhui Meng & Haoming Sun & Jiarong Xie & Chengjun Wang & Jiajing Wu & Yanqing Hu, 2021. "Preference for Number of Friends in Online Social Networks," Future Internet, MDPI, vol. 13(9), pages 1-13, September.
    7. Huang, Shuhong & Wang, Xiangrong & Peng, Liyang & Xie, Jiarong & Sun, Jiachen & Hu, Yanqing, 2021. "Optimal compression for bipartite networks," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).

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