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Local Variation of Hashtag Spike Trains and Popularity in Twitter

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  • Ceyda Sanlı
  • Renaud Lambiotte

Abstract

We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.

Suggested Citation

  • Ceyda Sanlı & Renaud Lambiotte, 2015. "Local Variation of Hashtag Spike Trains and Popularity in Twitter," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-18, July.
    • Handle: RePEc:plo:pone00:0131704
    DOI: 10.1371/journal.pone.0131704
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    References listed on IDEAS

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    1. Kazutoshi Sasahara & Yoshito Hirata & Masashi Toyoda & Masaru Kitsuregawa & Kazuyuki Aihara, 2013. "Quantifying Collective Attention from Tweet Stream," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
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    Cited by:

    1. Picoli, Sergio & Bombo, Giorgio & Santos, Edenize S.D. & Deprá, Pedro P. & Mendes, Renio S., 2022. "Characterizing postural sway signals by the analysis of zero-crossing patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Leihan Zhang & Ke Xu & Jichang Zhao, 2017. "Sleeping beauties in meme diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 383-402, July.

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