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Exploring the Complex Pattern of Information Spreading in Online Blog Communities

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  • Sen Pei
  • Lev Muchnik
  • Shaoting Tang
  • Zhiming Zheng
  • Hernán A Makse

Abstract

Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors.

Suggested Citation

  • Sen Pei & Lev Muchnik & Shaoting Tang & Zhiming Zheng & Hernán A Makse, 2015. "Exploring the Complex Pattern of Information Spreading in Online Blog Communities," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
  • Handle: RePEc:plo:pone00:0126894
    DOI: 10.1371/journal.pone.0126894
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    References listed on IDEAS

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    1. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
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    Cited by:

    1. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    2. Fu, Minglei & Yang, Hongbo & Feng, Jun & Guo, Wen & Le, Zichun & Lande, Dmytro & Manko, Dmytro, 2018. "Preferential information dynamics model for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 993-1005.
    3. Gao, Chao & Tang, Shaoting & Li, Weihua & Yang, Yaqian & Zheng, Zhiming, 2018. "Dynamical processes and epidemic threshold on nonlinear coupled multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 330-338.
    4. Ma, Jing & Li, Dandan & Tian, Zihao, 2016. "Rumor spreading in online social networks by considering the bipolar social reinforcement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 108-115.
    5. Zhao, Narisa & Cui, Xuelian, 2017. "Impact of individual interest shift on information dissemination in modular networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 232-242.
    6. Ren, Fei & Li, Sai-Ping & Liu, Chuang, 2017. "Information spreading on mobile communication networks: A new model that incorporates human behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 334-341.
    7. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    8. 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.

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