Users explain changes in Xiaohongshu audiences and manage community boundaries
Making Sense of Audience Change: Causal Attribution and Boundary Management on Xiaohongshu
Human-Computer InteractionArtificial IntelligenceComputers and Society
Summary
Social media platforms that show recommendations influence not just what people see but also who joins groups and how those groups stay connected. The authors studied conversations on Xiaohongshu during the 2022 World Cup when many new users appeared. They found that people explained changes by blaming either the recommendation algorithms or other users, and these explanations shaped how they tried to include or exclude others. For example, algorithm issues led to technical fixes, while blaming people led to social rules enforcement. This helps us see how audience shifts can create new challenges in online community control.
What this means in practice
- •For social media platform designers: Design tools to detect how users attribute audience changes and help shape boundary management strategies dynamically.
- •For community managers: Develop moderation approaches that align with users’ views about platform algorithms or social actors to better handle audience influx.
Tested on one dataset.
Authors
Zhiyi Zhou, Kyrie Zhou
Abstract
Recommendation-driven platforms can reshape not only what users see, but also who becomes visible and how community boundaries are negotiated. We examine Xiaohongshu discussions surrounding a perceived World Cup-related audience influx to understand how users attributed community change and how those explanations related to boundary-management strategies. We analyzed 262 comments from eight high-engagement threads using researcher-led thematic analysis, structured coding, and permutation-based association analysis. Users described personalization as both social separation and a boundary that could fail, interpreted platform growth through audience acquisition and commercialization, and contested whether gender should define community membership. Among 70 comments with explicit attributions and strategies, attribution and strategy were strongly associated (Cramer's V = .583): algorithmic attributions concentrated in technical boundary management, while social-actor attributions more often aligned with social enforcement or normative socialization. These findings connect causal sensemaking with boundary management and show how audience restructuring can create governance consequences on recommendation-driven platforms.