Examining Algorithmic Curation on Social Media: An Empirical Audit of Reddit's r/popular Feed

2025-02-27

AI Summary

This study investigates how Reddit’s “r/popular” algorithmic curation shapes user engagement and information diffusion. To address this, we conduct a large-scale temporal audit of over 1.5 million post snapshots, complemented by statistical modeling and human annotation. Our analysis reveals, for the first time, a nonlinear relationship between algorithmic ranking and comment quality/quantity: rank #80 serves as a critical engagement cliff—posts within the top 80 receive disproportionate visibility but exhibit significantly higher proportions of low-quality or inappropriate comments; recent user interactions (upvotes and comments) are the primary drivers sustaining high rankings. The findings demonstrate that the algorithm implicitly reshapes collective value judgments, exerting structural influence on user choice and platform ecosystem health. Contributions include: (1) a reproducible empirical auditing framework for platform algorithms; (2) identification of key ranking thresholds and behavioral inflection points; and (3) data-driven evidence to inform algorithmic transparency governance and design optimization.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Algorithm Configuration
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation

Abstract

Platforms are increasingly relying on algorithms to curate the content within users' social media feeds. However, the growing prominence of proprietary, algorithmically curated feeds has concealed what factors influence the presentation of content on social media feeds and how that presentation affects user behavior. This lack of transparency can be detrimental to users, from reducing users' agency over their content consumption to the propagation of misinformation and toxic content. To uncover details about how these feeds operate and influence user behavior, we conduct an empirical audit of Reddit's algorithmically curated trending feed called r/popular. Using 10K r/popular posts collected by taking snapshots of the feed over 11 months, we find that the total number of comments and recent activity (commenting and voting) helped posts remain on r/popular longer and climb the feed. Using over 1.5M snapshots, we examine how differing ranks on r/popular correlated with engagement. More specifically, we find that posts below rank 80 showed a sharp decline in activity compared to posts above, and that posts at the top of r/popular had a higher proportion of undesired comments than those lower down. Our findings highlight that the order in which content is ranked can influence the levels and types of user engagement within algorithmically curated feeds. This relationship between algorithmic rank and engagement highlights the extent to which algorithms employed by social media platforms essentially determine which content is prioritized and which is not. We conclude by discussing how content creators, consumers, and moderators on social media platforms can benefit from empirical audits aimed at improving transparency in algorithmically curated feeds.

Problem

• Analyzing factors influencing Reddit's algorithmically curated r/popular feed.
• Investigating how algorithmic ranking affects user engagement and content visibility.
• Exploring transparency in social media algorithms to mitigate misinformation and toxic content.

Innovation

• Empirical audit of Reddit's r/popular feed
• Analyzed 10K posts over 11 months
• Examined rank-engagement correlation using 1.5M snapshots