Local variation of hashtag spike trains and popularity in Twitter

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 dynamics, 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.

7 pages, 7 figures

References in corpus (6)

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Cited by in corpus (11)

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• Identifying exogenous and endogenous activity in social media
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• Application of optimal data-based binning method to spatial analysis of ecological datasets
• Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?
• A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics
• Interactional and Informational Attention on Twitter
• The statistical mechanics of Twitter
• Separating temporal and topological effects in walk-based network centrality
• Local Variation of Collective Attention in Hashtag Spike Trains