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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2017

07.03.2016 | Original Article

Precocious identification of popular topics on Twitter with the employment of predictive clustering

verfasst von: Vasilii A. Gromov, Anton S. Konev

Erschienen in: Neural Computing and Applications | Ausgabe 11/2017

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Abstract

The present paper outlines a novel approach to predict popularity of topics for social network Twitter; the method is designed to identify precociously the topics able to demonstrate “explosive” growth in popularity. First of all, the predictive clustering method ascertains real (not written in hash-tags!) topics of tweets and then predicts popularity rates for the topics. The same clustering algorithm is employed both to ascertain the real topic of a message and to cluster segments of time series (in order to predict topics popularity), namely, maximum likelihood adaptive neural system based upon modelling field theory. In the course of wide-ranging simulation, typical variants of “pre-explosive” dynamics were revealed; some of them were turned out to be equal to heuristic techniques to predict topics popularity well known for PR community collaborating with the network (“crab,” “Pesavento’s butterfly,” etc.).
Vorheriger Artikel Impulsive fractional-order neural networks with time-varying delays: almost periodic solutions
Nächster Artikel A novel hybrid algorithm for solving continuous single-objective defensive location problem

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Metadaten
Titel
Precocious identification of popular topics on Twitter with the employment of predictive clustering
verfasst von
Vasilii A. Gromov
Anton S. Konev
Publikationsdatum
07.03.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2256-1

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