在最近发表在EPJ Data Science 的研究中，我们用Twitch直播玩宝可梦游戏（Twitch Plays Pokémon，TPP）为例研究了这个可以聚集上百万人的社交场景。在这个场景中，创建者会开始一个宝可梦游戏，并通过Twitch（知名游戏直播平台）进行直播。与一般的玩线上游戏不同，创建者不会操纵游戏角色，而是将游戏端口与直播平台的聊天窗口链接，这样所有观看直播的人就可以通过聊天窗口输入口令来控制游戏人物。这样的结合产生了一个复杂的集体控制游戏，由上百万条意识共同合作来完成游戏。
Despite 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 Pokémon), 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.
