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

Research Poster Social & Behavioral Sciences 2025 Graduate Exhibition

Presentation by Ruyuan Wan

Exhibition Number 92

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.

Importance

Social media platforms use recommendation algorithms to personalize content, but these systems often limit users' control over who sees their posts. Our study explores how women on Xiaohongshu, a recommendation-driven social platform, creatively re-appropriate hashtags to manage their audience and maintain safe spaces. By strategically using hashtags unrelated to their content, they circumvent algorithmic visibility issues and reduce unwanted interactions. This research sheds light on how users adapt to opaque digital environments and highlights the role of grassroots strategies in shaping online safety and governance. Our findings contribute to broader discussions on platform design, digital agency, and the evolving dynamics of social media interactions.

DEI Statement

Our research examines how women on Xiaohongshu, a recommendation-driven social platform, strategically re-appropriate hashtags to control their audience and create safer online spaces. This practice is particularly significant for marginalized groups who face harassment, algorithmic bias, and limited agency in digital spaces. By highlighting how women navigate and resist algorithmic constraints, our work contributes to discussions on gendered experiences in social media governance, digital self-determination, and platform accountability. Our findings underscore the importance of user-driven strategies in fostering inclusivity and equity in online spaces, advancing conversations on feminist HCI, algorithmic bias, and community self-governance. This research offers valuable insights into how technology can better support diverse populations rather than reinforce existing inequalities.