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

TL;DR

This paper compares rule-based and neural network methods to analyze the social dynamics of Reddit's collaborative canvas r/place, revealing how predictability varies across space and time and highlighting different aspects of emergent object formation.

Contribution

It introduces and compares two novel approaches—cellular automata approximation and CNN modeling—to analyze the complex social dynamics in a large-scale online collaborative experiment.

Findings

Predictability of objects varies with context size in space and time.

A peak in difficulty to infer behavioral rules occurs mid-experiment.

Combining rule-based and neural methods reveals diverse social dynamics insights.

Abstract

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.