Hashtag Re-Appropriation for Audience Control on Recommendation-Driven Social Media Xiaohongshu (rednote)

2025-01-30

AI Summary

On algorithmic recommendation platforms such as Xiaohongshu, women and marginalized creators face diminished content visibility and unwanted exposure to non-target audiences (e.g., male users). This study identifies their strategic practice of “semantic decoupling via tag appropriation”—e.g., substituting sensitive topic tags with seemingly benign ones like #BabyFood—to deliberately circumvent algorithmic distribution and regulate visibility while reinforcing community boundaries. Combining computational analysis of 5,800 posts with in-depth interviews of 24 diverse users, we demonstrate that this tactic significantly reduces engagement from non-target audiences and strengthens perceived safety. We introduce the novel conceptual framework of “algorithmic agency,” revealing how marginalized users actively reconfigure and counter-govern platform algorithms. Our findings provide empirically grounded, user-centered insights to inform more transparent and inclusive algorithmic design and governance.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Algorithm Configuration
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

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

Abstract

Algorithms have played a central role in personalized recommendations on social media. However, they also present significant obstacles for content creators trying to predict and manage their audience reach. This issue is particularly challenging for marginalized groups seeking to maintain safe spaces. Our study explores how women on Xiaohongshu (rednote), a recommendation-driven social platform, proactively re-appropriate hashtags (e.g., #Baby Supplemental Food) by using them in posts unrelated to their literal meaning. The hashtags were strategically chosen from topics that would be uninteresting to the male audience they wanted to block. Through a mixed-methods approach, we analyzed the practice of hashtag re-appropriation based on 5,800 collected posts and interviewed 24 active users from diverse backgrounds to uncover users' motivations and reactions towards the re-appropriation. This practice highlights how users can reclaim agency over content distribution on recommendation-driven platforms, offering insights into self-governance within algorithmic-centered power structures.

Problem

• Content Visibility
• Algorithmic Recommendations
• Social Media Platforms

Innovation

• Creative Hashtag Manipulation
• Algorithmic Power Dynamics
• Privacy Control Strategies