Temporal Pattern of (Re)Tweets Reveal Cascade Migration

Ayan Kumar Bhowmick, Martin Gueuning, Jean-Charles Delvenne, Renaud Lambiotte, Bivas Mitra

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

Résumé

Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the Twitter follower network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks and cascade migration to a new locality as well as predict locality saturation from inter-retweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.

langue originaleAnglais
titreProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
rédacteurs en chefJana Diesner, Elena Ferrari, Guandong Xu
Lieu de publicationNew York, NY, USA
EditeurACM Press
Pages483-488
Nombre de pages6
ISBN (Electronique)9781450349932
ISBN (imprimé)978-1-4503-4993-2
Les DOIs
étatPublié - 31 juil. 2017

Série de publications

NomASONAM '17
EditeurACM

Empreinte digitale

Websites
Analytical models

mots-clés

    Citer ceci

    Bhowmick, A. K., Gueuning, M., Delvenne, J-C., Lambiotte, R., & Mitra, B. (2017). Temporal Pattern of (Re)Tweets Reveal Cascade Migration. Dans J. Diesner, E. Ferrari, & G. Xu (eds.), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 (p. 483-488). (ASONAM '17). New York, NY, USA: ACM Press. https://doi.org/10.1145/3110025.3110084, https://doi.org/10.1145/3110025.3110084
    Bhowmick, Ayan Kumar ; Gueuning, Martin ; Delvenne, Jean-Charles ; Lambiotte, Renaud ; Mitra, Bivas. / Temporal Pattern of (Re)Tweets Reveal Cascade Migration. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Editeur / Jana Diesner ; Elena Ferrari ; Guandong Xu. New York, NY, USA : ACM Press, 2017. p. 483-488 (ASONAM '17).
    @inproceedings{99e0159d7565442f86f8088ea89bb15d,
    title = "Temporal Pattern of (Re)Tweets Reveal Cascade Migration",
    abstract = "Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the Twitter follower network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks and cascade migration to a new locality as well as predict locality saturation from inter-retweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.",
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    author = "Bhowmick, {Ayan Kumar} and Martin Gueuning and Jean-Charles Delvenne and Renaud Lambiotte and Bivas Mitra",
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    Bhowmick, AK, Gueuning, M, Delvenne, J-C, Lambiotte, R & Mitra, B 2017, Temporal Pattern of (Re)Tweets Reveal Cascade Migration. Dans J Diesner, E Ferrari & G Xu (eds), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. ASONAM '17, ACM Press, New York, NY, USA, p. 483-488. https://doi.org/10.1145/3110025.3110084, https://doi.org/10.1145/3110025.3110084

    Temporal Pattern of (Re)Tweets Reveal Cascade Migration. / Bhowmick, Ayan Kumar; Gueuning, Martin; Delvenne, Jean-Charles; Lambiotte, Renaud; Mitra, Bivas.

    Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Ed. / Jana Diesner; Elena Ferrari; Guandong Xu. New York, NY, USA : ACM Press, 2017. p. 483-488 (ASONAM '17).

    Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

    TY - GEN

    T1 - Temporal Pattern of (Re)Tweets Reveal Cascade Migration

    AU - Bhowmick, Ayan Kumar

    AU - Gueuning, Martin

    AU - Delvenne, Jean-Charles

    AU - Lambiotte, Renaud

    AU - Mitra, Bivas

    PY - 2017/7/31

    Y1 - 2017/7/31

    N2 - Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the Twitter follower network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks and cascade migration to a new locality as well as predict locality saturation from inter-retweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.

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    KW - Inter-retweet intervals

    KW - Pivotal user

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    Bhowmick AK, Gueuning M, Delvenne J-C, Lambiotte R, Mitra B. Temporal Pattern of (Re)Tweets Reveal Cascade Migration. Dans Diesner J, Ferrari E, Xu G, rédacteurs en chef, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. New York, NY, USA: ACM Press. 2017. p. 483-488. (ASONAM '17). https://doi.org/10.1145/3110025.3110084, https://doi.org/10.1145/3110025.3110084