Identifying exogenous and endogenous activity in social media

Kazuki Fujita, Alexey Medvedev, Shinsuke Koyama, Renaud Lambiotte, Shigeru Shinomoto

Résultats de recherche: Contribution à un journal/une revueArticle

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Résumé

The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions of exogenous, e.g. news media, and endogenous, e.g. information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.
langue originaleAnglais
Nombre de pages8
journalArXiv pre-print
étatPublié - 2 août 2018

Empreinte digitale

social network
marketing
time series
social media
method
test

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    title = "Identifying exogenous and endogenous activity in social media",
    abstract = "The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions of exogenous, e.g. news media, and endogenous, e.g. information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.",
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    Identifying exogenous and endogenous activity in social media. / Fujita, Kazuki; Medvedev, Alexey; Koyama, Shinsuke; Lambiotte, Renaud; Shinomoto, Shigeru.

    Dans: ArXiv pre-print, 02.08.2018.

    Résultats de recherche: Contribution à un journal/une revueArticle

    TY - JOUR

    T1 - Identifying exogenous and endogenous activity in social media

    AU - Fujita, Kazuki

    AU - Medvedev, Alexey

    AU - Koyama, Shinsuke

    AU - Lambiotte, Renaud

    AU - Shinomoto, Shigeru

    N1 - 5 figures

    PY - 2018/8/2

    Y1 - 2018/8/2

    N2 - The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions of exogenous, e.g. news media, and endogenous, e.g. information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.

    AB - The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions of exogenous, e.g. news media, and endogenous, e.g. information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.

    KW - physics.soc-ph

    KW - cs.SI

    M3 - Article

    JO - ArXiv pre-print

    JF - ArXiv pre-print

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