A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics

Le Zhi Wang, Zhi Dan Zhao, Junjie Jiang, Bing Hui Guo, Xiao Wang, Zi Gang Huang, Ying-Cheng Lai

    Research output: Contribution to journalArticle

    Abstract

    We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

    Original languageEnglish (US)
    Article number023136
    JournalChaos
    Volume29
    Issue number2
    DOIs
    StatePublished - Feb 1 2019

    Fingerprint

    Social Networking
    Growth Model
    Distinct
    Social Behavior
    Model
    Population Growth
    Human Factors
    Cell Population
    Hybrid Model
    Ecology
    Biology
    Web Services
    web services
    Recommendations
    China
    Sharing
    ecology
    websites
    Human
    Robustness

    ASJC Scopus subject areas

    • Statistical and Nonlinear Physics
    • Mathematical Physics
    • Physics and Astronomy(all)
    • Applied Mathematics

    Cite this

    A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics. / Wang, Le Zhi; Zhao, Zhi Dan; Jiang, Junjie; Guo, Bing Hui; Wang, Xiao; Huang, Zi Gang; Lai, Ying-Cheng.

    In: Chaos, Vol. 29, No. 2, 023136, 01.02.2019.

    Research output: Contribution to journalArticle

    Wang, Le Zhi ; Zhao, Zhi Dan ; Jiang, Junjie ; Guo, Bing Hui ; Wang, Xiao ; Huang, Zi Gang ; Lai, Ying-Cheng. / A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics. In: Chaos. 2019 ; Vol. 29, No. 2.
    @article{c6ad6f618e0e42c197ac978186eb00aa,
    title = "A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics",
    abstract = "We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an {"}S{"}-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.",
    author = "Wang, {Le Zhi} and Zhao, {Zhi Dan} and Junjie Jiang and Guo, {Bing Hui} and Xiao Wang and Huang, {Zi Gang} and Ying-Cheng Lai",
    year = "2019",
    month = "2",
    day = "1",
    doi = "10.1063/1.5085009",
    language = "English (US)",
    volume = "29",
    journal = "Chaos (Woodbury, N.Y.)",
    issn = "1054-1500",
    publisher = "American Institute of Physics Publising LLC",
    number = "2",

    }

    TY - JOUR

    T1 - A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics

    AU - Wang, Le Zhi

    AU - Zhao, Zhi Dan

    AU - Jiang, Junjie

    AU - Guo, Bing Hui

    AU - Wang, Xiao

    AU - Huang, Zi Gang

    AU - Lai, Ying-Cheng

    PY - 2019/2/1

    Y1 - 2019/2/1

    N2 - We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

    AB - We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

    UR - http://www.scopus.com/inward/record.url?scp=85062440081&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85062440081&partnerID=8YFLogxK

    U2 - 10.1063/1.5085009

    DO - 10.1063/1.5085009

    M3 - Article

    VL - 29

    JO - Chaos (Woodbury, N.Y.)

    JF - Chaos (Woodbury, N.Y.)

    SN - 1054-1500

    IS - 2

    M1 - 023136

    ER -