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

TL;DR

This study explores how interactions with large language models (LLMs) enable women on social media to share gendered experiences more confidently by addressing internal standards and concerns, thus supporting digital feminism.

Contribution

It uncovers the role of LLMs in helping women articulate and share gendered experiences, highlighting a new practice on Chinese social media that leverages AI for personal expression.

Findings

Women use LLMs to meet self-imposed sharing standards

LLMs help women navigate concerns about judgment and appropriateness

Interaction with LLMs facilitates more open sharing of gendered experiences

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.