Unveiling Scaling Laws in the Regulatory Functions of Reddit

TL;DR

Analysis of Reddit's regulatory functions reveals robust scaling relationships across different subreddits, suggesting universal patterns between community size and the amount of regulation needed, and suggests that a more proactive moderation approach, characterized by increased bot activity and moderator comment removals, tends to result in less user engagement under the scaling framework.

Abstract

Online platforms like Reddit, Wikipedia, and Facebook are integral to modern life, enabling content creation and sharing through posts, comments, and discussions. Despite their virtual and often anonymous nature, these platforms need rules and oversight to maintain a safe and productive environment. As these communities grow, a key question arises: how does the need for regulatory functions scale? Do larger groups require more regulatory actions and oversight per person, or can they manage with less? Our analysis of Reddit's regulatory functions reveals robust scaling relationships across different subreddits, suggesting universal patterns between community size and the amount of regulation needed. We found that the number of comments and moderator actions, such as comment removals, grew faster than the community size, with superlinear exponents of 1.12 and 1.18, respectively. However, bot-based rule enforcement did not keep pace with community growth, exhibiting a slightly sublinear exponent of 0.95. Further analysis of the residuals from these scaling behaviors identified a 'trade-off axis,' where one-way coordination mechanisms (bots and moderators) counteract two-way interactions (comments) and vice versa. Our findings suggest that a more proactive moderation approach, characterized by increased bot activity and moderator comment removals, tends to result in less user engagement under the scaling framework. Understanding these natural scaling patterns and interactions can help platform administrators and policymakers foster healthy online communities while mitigating harmful behaviors such as harassment, doxxing, and misinformation. Without proper regulation, these negative behaviors can proliferate and cause significant damage. Targeted interventions based on these insights are key to ensuring online platforms remain safe and beneficial spaces.

Figures (3)

• Figure 1: Regulatory Functions in Reddit In Reddit, subreddits provide spaces for discussions on shared topics. (a) In the subreddit r/news, users discuss Biden's election as the US president, where the engagement is quantified through the number of comments. (b) Bots ensure adherence to rules through automated comments. (c) Moderators enforce guidelines by removing comments that violate rules. (d) Summary of regulatory functions and associated metrics.
• Figure 2: Scaling Behavior of Regulatory Functions in Reddit (a) Descriptive statistics of our sampled Subreddits (b) Mutual interactions (# of comments), (c) Supervision (# of removed comments), and (d) Rule Enforcement (# of bot comments) exhibit scaling behavior with unique users. Each subreddit is log-binned (error bars in blue), and the $\beta$ in $N^\beta$ is estimated, depicted by the orange line. The gray line shows a linear trend for reference. Our findings indicate that mutual interaction and supervision demonstrate superlinear scaling, growing faster than the number of users, while rule enforcement exhibits slightly sublinear, very close to linear scaling, increasing proportionally to user numbers.
• Figure 3: Principal Component Analysis of Subreddit Categories PC1 represents the "Controversy Axis," and PC2 signifies the "Trade-off Axis," exploring a trade-off among regulatory functions. Grey dots denote average residual values for each subreddit category, with bolded categories identified. Arrows illustrate variable loadings on the principal components, with positive loadings on PC1 and a mix of positive and negative loadings on PC2, indicating an interplay between functions.