"Without AI, I Would Never Share This Online": Unpacking How LLMs Catalyze Women's Sharing of Gendered Experiences on Social Media

2026-02-25

AI Summary

This study investigates how large language models (LLMs) assist women in overcoming internal and external barriers when sharing gendered experiences on social media. Drawing on semi-structured interviews with 20 women who post such content on Xiaohongshu, complemented by content analysis, the research reveals that LLMs not only alleviate concerns about external judgment but also serve as mediating tools for reflection and expression. By helping users meet their self-imposed narrative standards, LLMs significantly enhance their confidence in storytelling and willingness to share. These findings offer a novel perspective within digital feminism, highlighting the role of AI in empowering individual expression and reshaping the dynamics of gendered discourse in online spaces.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Sharing gendered experiences on social media has been widely recognized as supporting women's personal sense-making and contributing to digital feminism. However, there are known concerns, such as fear of judgment and backlash, that may discourage women from posting online. In this study, we examine a recurring practice on Xiaohongshu, a popular Chinese social media platform, in which women share their gendered experiences alongside screenshots of conversations with LLMs. We conducted semi-structured interviews with 20 women to investigate whether and how interactions with LLMs might support women in articulating and sharing gendered experiences. Our findings reveal that, beyond those external concerns, women also hold self-imposed standards regarding what feels appropriate and worthwhile to share publicly. We further show how interactions with LLMs help women meet these standards and navigate such concerns. We conclude by discussing how LLMs might be carefully and critically leveraged to support women's everyday expression online.

Problem

• gendered experiences
• social media sharing
• fear of judgment
• self-imposed standards
• women's expression

Innovation

• LLMs
• gendered experiences
• social media
• digital feminism
• human-AI interaction