Local Variation of Hashtag Spike Trains and Popularity in Twitter.
Where this comes from
- Record sourced from PubMed, PMID 26161650.
- Also identified by DOI 10.1371/journal.pone.0131704 and PMC identifier 4498919.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Algorithms
- Models, Theoretical
- Social Media