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000070263 0247_ $$2doi$$a10.1155/2018/4212509
000070263 0248_ $$2sideral$$a105369
000070263 037__ $$aART-2018-105369
000070263 041__ $$aeng
000070263 100__ $$0(orcid)0000-0002-3366-4706$$aAguilera, M.
000070263 245__ $$aRhythms of the Collective Brain: Metastable Synchronization and Cross-Scale Interactions in Connected Multitudes
000070263 260__ $$c2018
000070263 5060_ $$aAccess copy available to the general public$$fUnrestricted
000070263 5203_ $$aCrowd behaviour challenges our fundamental understanding of social phenomena. Involving complex interactions between multiple temporal and spatial scales of activity, its governing mechanisms defy conventional analysis. Using 1.5 million Twitter messages from the 15M movement in Spain as an example of multitudinous self-organization, we describe the coordination dynamics of the system measuring phase-locking statistics at different frequencies using wavelet transforms, identifying 8 frequency bands of entrained oscillations between 15 geographical nodes. Then we apply maximum entropy inference methods to describe Ising models capturing transient synchrony in our data at each frequency band. The models show that (1) all frequency bands of the system operate near critical points of their parameter space and (2) while fast frequencies present only a few metastable states displaying all-or-none synchronization, slow frequencies present a diversity of metastable states of partial synchronization. Furthermore, describing the state at each frequency band using the energy of the corresponding Ising model, we compute transfer entropy to characterize cross-scale interactions between frequency bands, showing (1) a cascade of upward information flows in which each frequency band influences its contiguous slower bands and (2) downward information flows where slow frequencies modulate distant fast frequencies.
000070263 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FFI2014-52173-P$$9info:eu-repo/grantAgreement/ES/MINECO/PSI2014-62092-EXP$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R
000070263 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000070263 590__ $$a2.591$$b2018
000070263 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b21 / 105 = 0.2$$c2018$$dQ1$$eT1
000070263 591__ $$aMULTIDISCIPLINARY SCIENCES$$b24 / 69 = 0.348$$c2018$$dQ2$$eT2
000070263 592__ $$a0.535$$b2018
000070263 593__ $$aMultidisciplinary$$c2018$$dQ1
000070263 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000070263 773__ $$g2018 (2018), 4212509 [9 pp]$$pComplexity$$tComplexity$$x1076-2787
000070263 8564_ $$s2223355$$uhttps://zaguan.unizar.es/record/70263/files/texto_completo.pdf$$yVersión publicada
000070263 8564_ $$s24492$$uhttps://zaguan.unizar.es/record/70263/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000070263 909CO $$ooai:zaguan.unizar.es:70263$$particulos$$pdriver
000070263 951__ $$a2020-01-17-22:03:46
000070263 980__ $$aARTICLE