Lately, studying social dynamics in interacting agents has been boosted by the power of computer models, which bring the richness of qualitative work, while offering the precision, transparency, extensiveness, and replicability of statistical and mathematical approaches. A particular set of phenomena for the study of social dynamics is Web collaborative platforms. A dataset of interest is r/place, a collaborative social experiment held in 2017 on Reddit, which consisted of a shared online canvas of 1000 pixels by 1000 pixels co-edited by over a million recorded users over 72 hours. In this paper, we designed and compared two methods to analyze the dynamics of this experiment. Our first method consisted in approximating the set of 2D cellular-automata-like rules used to generate the canvas images and how these rules change over time. The second method consisted in a convolutional neural network (CNN) that learned an approximation to the generative rules in order to generate the complex outcomes of the canvas. Our results indicate varying context-size dependencies for the predictability of different objects in r/place in time and space. They also indicate a surprising peak in difficulty to statistically infer behavioral rules towards the middle of the social experiment, while user interactions did not drop until before the end. The combination of our two approaches, one rule-based and the other statistical CNN-based, shows the ability to highlight diverse aspects of analyzing social dynamics.
翻译:最近,由于计算机模型的力量,对互动剂中的社会动态进行了研究,这些模型带来了丰富的定性工作,同时提供了统计和数学方法的精确性、透明度、广度和可复制性。用于研究社会动态的一套特殊现象是网络协作平台。一个感兴趣的数据集是2017年在Reddit上进行的合作性社会实验,其中包括由100多万记录用户共同编辑超过72小时的1000个像素组成的共享在线画册。在本文中,我们设计并比较了两种方法来分析这一实验的动态。我们的第一种方法是接近2D型蜂窝-蜂窝式规则,用来生成博览图像,以及这些规则如何随时间变化。第二个方法是动态图集/地点。在2017年在Reddit上进行的一项合作性社会实验,该实验由1 000个像素组成,由1 000个像素组成,由100多万记录用户共同编辑,在72小时以上。在本文中,我们设计并比较了两种方法来分析这一实验的动态。我们的第一种方法是近似于2D型蜂窝-蜂窝式规则的相似性规则的近似。在统计上展示了两种互动能力的高峰中,在统计学上展示了两种方法的两种不同的社会动态上不至最后的混合。