arXiv subjects

Qunfang Wu

Publications and source records attributed to Qunfang Wu.

5 recordsLinked to original sources

arXiv2025

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

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.

cs.HC↗

arXiv2025

Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content

As personalized recommendation algorithms become integral to social media platforms, users are increasingly aware of their ability to influence recommendation content. However, limited research has explored how users provide feedback through their behaviors and platform mechanisms to shape the recommendation content. We conducted semi-structured interviews with 34 active users of algorithmic-driven social media platforms (e.g., Xiaohongshu, Douyin). In addition to explicit and implicit feedback, this study introduced intentional implicit feedback, highlighting the actions users intentionally took to refine recommendation content through perceived feedback mechanisms. Additionally, choices of feedback behaviors were found to align with specific purposes. Explicit feedback was primarily used for feed customization, while unintentional implicit feedback was more linked to content consumption. Intentional implicit feedback was employed for multiple purposes, particularly in increasing content diversity and improving recommendation relevance. This work underscores the user intention dimension in the explicit-implicit feedback dichotomy and offers insights for designing personalized recommendation feedback that better responds to users' needs.

cs.HC↗

arXiv2024

Designing an Intervention Tool for End-User Algorithm Audits in Personalized Recommendation Systems

As algorithms increasingly shape user experiences on personalized recommendation platforms, there is a growing need for tools that empower end users to audit these algorithms for potential bias and harms. This paper introduces a novel intervention tool, MapMyFeed, designed to support everyday user audits. The tool addresses key challenges associated with user-driven algorithm audits, such as low algorithm literacy, unstructured audit paths, and the presence of noise. MapMyFeed assists users by offering guiding prompts, tracking audit paths via a browser extension, and visualizing audit results through a live dashboard. The tool will not only foster users' algorithmic literacy and awareness but also enhance more transparent and fair recommendation systems.

cs.HC↗

arXiv2024

Dismantling Gender Blindness in Online Discussion of a Crime/Gender Dichotomy

Contemporary feminists utilize social media for activism, while backlashes come along. The gender-related discourses are often diminished when addressing public events regarding sexism and gender inequality on social media platforms. The dichotomized debate around the Tangshan beating incident in China epitomized how criminal interpretations of gender-related violence became a backlash against feminist expressions. By analyzing posts on Weibo using mixed methods, we describe the emerging discursive patterns around crime and gender, uncovering the inherent gender-blind sexism that refutes feminist discourses on the social platform. We also highlight the critical restrictions facing grassroots feminist activism in Chinese cyberspace and propose implications for the design and research related to digital feminist activism.

cs.HC↗

arXiv2022

Malicious Selling Strategies in E-Commerce Livestream: A Case Study of Alibaba's Taobao and ByteDance's TikTok

Due to the limitations imposed by the COVID-19 pandemic, many users have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, many streamers' malicious selling behaviors have been reported. In this research, we sought to explore streamers' malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China -- Taobao and TikTok (or "Douyin" in Chinese). We identified 16 malicious selling strategies and found that platform designs enhanced these malicious selling strategies. Second, through an interview study with 13 viewers, we provide a rich description of viewers' awareness of malicious selling strategies and the challenges they encountered while trying to overcome malicious selling. We conclude by discussing the policy and design implications of countering malicious selling.

cs.CR↗