Local variation of collective attention in hashtag spike trains

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Résumé

In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.

langue originaleAnglais
titreAAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media
Sous-titrePapers from the 2015 ICWSM Workshop
EditeurAI Access Foundation
Pages8-12
Nombre de pages5
VolumeWS-15-17
ISBN (imprimé)9781577357353
étatPublié - 22 avr. 2015
Evénement9th International Conference on Web and Social Media, ICWSM 2015 - Oxford, Royaume-Uni
Durée: 26 mai 201529 mai 2015

Une conférence

Une conférence9th International Conference on Web and Social Media, ICWSM 2015
PaysRoyaume-Uni
La villeOxford
période26/05/1529/05/15

Empreinte digitale

Neurons
Time series
Time delay

mots-clés

    Citer ceci

    Sanli Cakir, C., & Lambiotte, R. (2015). Local variation of collective attention in hashtag spike trains. Dans AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop (Vol WS-15-17, p. 8-12). AI Access Foundation.
    Sanli Cakir, Céyda ; Lambiotte, Renaud. / Local variation of collective attention in hashtag spike trains. AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop. Vol WS-15-17 AI Access Foundation, 2015. p. 8-12
    @inproceedings{d039ddf0d14e46cca0ba2c85f6144b51,
    title = "Local variation of collective attention in hashtag spike trains",
    abstract = "In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.",
    keywords = "Twitter, data mining, online social media, behavior, statistical signal processing",
    author = "{Sanli Cakir}, C{\'e}yda and Renaud Lambiotte",
    year = "2015",
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    booktitle = "AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media",
    publisher = "AI Access Foundation",

    }

    Sanli Cakir, C & Lambiotte, R 2015, Local variation of collective attention in hashtag spike trains. Dans AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop. VOL. WS-15-17, AI Access Foundation, p. 8-12, 9th International Conference on Web and Social Media, ICWSM 2015, Oxford, Royaume-Uni, 26/05/15.

    Local variation of collective attention in hashtag spike trains. / Sanli Cakir, Céyda; Lambiotte, Renaud.

    AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop. Vol WS-15-17 AI Access Foundation, 2015. p. 8-12.

    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 - Local variation of collective attention in hashtag spike trains

    AU - Sanli Cakir, Céyda

    AU - Lambiotte, Renaud

    PY - 2015/4/22

    Y1 - 2015/4/22

    N2 - In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.

    AB - In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.

    KW - Twitter

    KW - data mining

    KW - online social media

    KW - behavior

    KW - statistical signal processing

    UR - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10631

    M3 - Conference contribution

    SN - 9781577357353

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    EP - 12

    BT - AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media

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    ER -

    Sanli Cakir C, Lambiotte R. Local variation of collective attention in hashtag spike trains. Dans AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop. Vol WS-15-17. AI Access Foundation. 2015. p. 8-12