The dynamics of information-driven coordination phenomena: A transfer entropy analysis

• Published in: Science advances Vol. 2; no. 4; p. e1501158
• Main Authors: Borge-Holthoefer, Javier, Perra, Nicola, Gonçalves, Bruno, González-Bailón, Sandra, Arenas, Alex, Moreno, Yamir, Vespignani, Alessandro
• Format: Journal Article
• Language: English
• Published: United States Science Advances 01.04.2016; American Association for the Advancement of Science
• Subjects: Algorithms; Collective behavior; collective phenomena; Comportament col·lectiu; Comportamiento colectivo; dynamical transitions; entropia de transferència; Entropy; entropía de transferencia; fenòmens col·lectius; fenómenos colectivos; Heterogeneous-Nuclear Ribonucleoproteins; Humans; Information Science; Models, Theoretical; Nonlinear Dynamics; Online social networks; Redes sociales en línea; SciAdv r-articles; Social Media; transfer entropy; transiciones dinámicas; transicions dinàmiques; Xarxes socials en línia; collective phenomena; transfer entropy; dynamical transitions
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.1501158

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.