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Reconstructing news spread networks and studying its dynamics

Reconstructing news spread networks and studying its dynamics News spread in internet media outlets can be seen as a contagious process forming temporal networks representing the influence between published articles. In this article we propose a methodology based on the application of natural language analysis of the articles to reconstruct the news spread network. From the reconstructed network, we show that the dynamics of the news spread can be approximated by a classical SIR epidemiological dynamics upon the network. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Reconstructing news spread networks and studying its dynamics

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Austria, part of Springer Nature
Subject
Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Sciences, Humanities, Law; Methodology of the Social Sciences
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-017-0483-9
Publisher site
See Article on Publisher Site

Abstract

News spread in internet media outlets can be seen as a contagious process forming temporal networks representing the influence between published articles. In this article we propose a methodology based on the application of natural language analysis of the articles to reconstruct the news spread network. From the reconstructed network, we show that the dynamics of the news spread can be approximated by a classical SIR epidemiological dynamics upon the network.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Jan 17, 2018

References