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.

arXiv comment7 pages, 7 figures

References in corpus (24)

• The origin of bursts and heavy tails in human dynamics
• The Dynamics of Protest Recruitment through an Online Network
• Virality Prediction and Community Structure in Social Networks
• A Poissonian explanation for heavy-tails in e-mail communication
• The Anatomy of a Scientific Rumor
• Characterizing and modeling the dynamics of online popularity
• Structural and Dynamical Patterns on Online Social Networks: the Spanish May 15th Movement as a case study
• Circadian pattern and burstiness in mobile phone communication
• Competition-induced criticality in a model of meme popularity
• Threshold model of cascades in temporal networks
• The Dynamics of Book Sales: Endogenous versus Exogenous Shocks in Complex Networks
• Burstiness and spreading on temporal networks
• How memory generates heterogeneous dynamics in temporal networks
• Effects of temporal correlations on cascades: Threshold models on temporal networks
• Correlated bursts and the role of memory range
• Voter model with non-Poissonian interevent intervals
• Cumulants of Hawkes point processes
• The Bursty Dynamics of the Twitter Information Network
• Predictability of extreme events in social media
• Modelling bursty time series
• A stochastic model of the tweet diffusion on the Twitter network
• Emergent user behavior on Twitter modelled by a stochastic differential equation
• Attention Competition with Advertisement
• Bursting transition in a linear self-exciting point process

Cited by in corpus (11)

• Modelling structure and predicting dynamics of discussion threads in online boards
• Temporal dynamics of online petitions
• Identifying exogenous and endogenous activity in social media
• Input-output relationship in social communications characterized by spike train analysis
• 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