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When AI Joins the Thread: A Computational Analysis of Gendered Human-AI Interactions on Weibo
Book chapter

When AI Joins the Thread: A Computational Analysis of Gendered Human-AI Interactions on Weibo

Chenxi Li, Zeqiang Wang, Yujia Wang, Jon Johnson, Suparna De and Zixi Chen
Social Computing, pp.137-150
Communications in Computer and Information Science, Springer Nature Singapore
2027

Abstract

China Gender Large language models (LLM) Social media Topic modeling
This study investigates how a Weibo-embedded artificial intelligence (AI) chatbot, CommentR, functions within a gendered communicative environment by systematically mapping the conversational contexts and linguistic patterns through which gendered dynamics manifest in human-AI interaction. Drawing on a large-scale dataset of interactions between Weibo users and CommentR, the study identifies the dominant thematic structures and user intentions that characterize engagement with the chatbot, while quantifying stylistic variations in its replies across user genders. Building on a three-level mechanism that links cultural, social, and psychological processes, the analysis shows how gendered meanings emerge through subtle linguistic differences across interactional contexts. Through the integration of computational methods and large language model (LLM)-based annotation, the study demonstrates how these subtle linguistic features vary systematically by user gender. These findings provide an empirical basis for understanding how everyday exchanges with AI systems both reproduce and subtly renegotiate gendered communication norms within China’s digital public sphere.

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