000079763 001__ 79763 000079763 005__ 20200716101506.0 000079763 0247_ $$2doi$$a10.1140/epjds/s13688-019-0200-1 000079763 0248_ $$2sideral$$a112663 000079763 037__ $$aART-2019-112663 000079763 041__ $$aeng 000079763 100__ $$0(orcid)0000-0002-1192-8707$$aAleta, A.$$uUniversidad de Zaragoza 000079763 245__ $$aThe dynamics of collective social behavior in a crowd controlled game 000079763 260__ $$c2019 000079763 5060_ $$aAccess copy available to the general public$$fUnrestricted 000079763 5203_ $$aDespite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication means has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokemon), in which nearly a million players participated during more than two weeks. Unlike other online games, in this event all the players controlled exactly the same character and thus it represents an exceptional example of a collective mind working to achieve a certain goal. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that having a fraction of players who do not follow the crowd''s average behavior is key to succeed in the game. The latter finding can be well explained by an nth order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. We show that the introduction of this system clearly polarized the community, splitting it in two. Finally, we discuss one of the peculiarities of these groups in the light of the social identity theory, which appears to describe well some of the observed dynamics. 000079763 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000079763 590__ $$a2.873$$b2019 000079763 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b19 / 106 = 0.179$$c2019$$dQ1$$eT1 000079763 591__ $$aSOCIAL SCIENCES, MATHEMATICAL METHODS$$b8 / 51 = 0.157$$c2019$$dQ1$$eT1 000079763 592__ $$a0.903$$b2019 000079763 593__ $$aComputer Science Applications$$c2019$$dQ1 000079763 593__ $$aModeling and Simulation$$c2019$$dQ1 000079763 593__ $$aComputational Mathematics$$c2019$$dQ2 000079763 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000079763 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza 000079763 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica 000079763 773__ $$g8 (2019), 22 [16 pp]$$pEPJ data sci.$$tEPJ Data Science$$x2193-1127 000079763 8564_ $$s1751165$$uhttps://zaguan.unizar.es/record/79763/files/texto_completo.pdf$$yVersión publicada 000079763 8564_ $$s89365$$uhttps://zaguan.unizar.es/record/79763/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000079763 909CO $$ooai:zaguan.unizar.es:79763$$particulos$$pdriver 000079763 951__ $$a2020-07-16-09:16:22 000079763 980__ $$aARTICLE