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Lately, studying social dynamics in interacting agents has been boosted bythe power of computer models, which bring the richness of qualitative work,while offering the precision, transparency, extensiveness, and replicability ofstatistical and mathematical approaches. A particular set of phenomena for thestudy of social dynamics is Web collaborative platforms. A dataset of interestis r/place, a collaborative social experiment held in 2017 on Reddit, whichconsisted of a shared online canvas of 1000 pixels by 1000 pixels co-edited byover a million recorded users over 72 hours. In this paper, we designed andcompared two methods to analyze the dynamics of this experiment. Our firstmethod consisted in approximating the set of 2D cellular-automata-like rulesused to generate the canvas images and how these rules change over time. Thesecond method consisted in a convolutional neural network (CNN) that learned anapproximation to the generative rules in order to generate the complex outcomesof the canvas. Our results indicate varying context-size dependencies for thepredictability of different objects in r/place in time and space. They alsoindicate a surprising peak in difficulty to statistically infer behavioralrules towards the middle of the social experiment, while user interactions didnot drop until before the end. The combination of our two approaches, onerule-based and the other statistical CNN-based, shows the ability to highlightdiverse aspects of analyzing social dynamics.

Two Ways of Understanding Social Dynamics: Analyzing the Predictability  of Emergence of Objects in Reddit r/place Dependent on Locality in Space and  Time