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10.1371/journal.pone.0131704 http://scihub22266oqcxt.onion/10.1371/journal.pone.0131704 C4498919!4498919!26161650     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2015 ; 10 (7): ä Nephropedia Template TP {{tp|p=26161650|t=2015. Local Variation of Hashtag Spike Trains and Popularity in Twitter |pdf=|usr=}}{{26161650}} gab.com Text Local Variation of Hashtag Spike Trains and Popularity in Twitter JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26161650 #FOAvip 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. Twit Text FOAVip Local Variation of Hashtag Spike Trains and Popularity in Twitter ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26161650 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26161650 #FOAvip English Wikipedia <ref>{{Cite pmid|26161650}}</ref> Local Variation of Hashtag Spike Trains and Popularity in Twitter #MMPMID26161650 Sanl? C; Lambiotte R PLoS One 2015[]; 10 (7): ä PMID26161650show ga 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. äLocal Variation of Hashtag Spike Trains and Popularity in Twitter (epmc).pdf DeepDyve Pubget Overpricing