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Resumen: Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. 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 microblogging time series to extract directed networks of influence among geolocalized subunits 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 subunits. In the absence of 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.
Idioma: Inglés
DOI: 10.1126/sciadv.1501158
Año: 2016
Publicado en: Science Advances 2, 4 (2016), e1501158 [9 pp]
ISSN: 2375-2548
Factor impacto SCIMAGO: 4.8 - Medicine (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/FENOL-GROUP
Financiación: info:eu-repo/grantAgreement/EC/FP7/317532/EU/Foundational Research on MULTIlevel comPLEX networks and systems/MULTIPLEX
Financiación: info:eu-repo/grantAgreement/ES/MINECO/FIS2011-25167
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)

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