Borge-Holthoefer, Javier, Perra, Nicola ORCID: https://orcid.org/0000-0002-5559-3064, Goncalves, Bruno, Gonzales-Bailon, Sandra, Arenas, Alex, Moreno, Yamir and Vespignani, Alessandro (2016) The dynamics of information-driven coordination phenomena: a transfer entropy analysis. Science Advances, 2 (4):e1501158. ISSN 2375-2548 (doi:10.1126/sciadv.1501158)

Preview	PDF (Publisher PDF) 14613_Perra_The dynamics of information driven (pub PDF OA) 2016.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. Download (2MB) | Preview
Preview	PDF (Author Accepted Manuscript) 14613_PERRA_The_Dynamics_of_Information_2016.pdf - Accepted Version Download (2MB)
	PDF (Email of Acceptance) 14613_PERRA_&_MORENO_Acceptance_Email_2016.pdf - Accepted Version Restricted to Repository staff only Download (188kB)

Abstract

Data from social media are providing unprecedented opportunities to investigate the processes that rule the dynamics of collective social phenomena. Here, we consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of micro-blogging time series to extract directed networks of influence among geolocalized sub-units in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time-scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social sub-units. In the absence of a clear exogenous driving, social collective phenomena can be represented as endogenously-driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Item Type:	Article
Additional Information:	2016 © The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
Uncontrolled Keywords:	Social Phenomena; Symbolic Transfer Entropy
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified:	21 Oct 2020 10:05
URI:	http://gala.gre.ac.uk/id/eprint/14613

Actions (login required)

View Item

Downloads