Marriage Discourse on Chinese Social Media: An LLM-assisted Analysis
Frank Tian-Fang Ye (1), Xiaozi Gao (2) ((1) Division of Social Sciences, The HKU SPACE Community College, Hong Kong SAR, PRC (2) Department of Early Childhood Education, Education University of Hong Kong, Hong Kong SAR, PRC)

TL;DR
This study analyzes a large dataset of Chinese social media posts to understand sentiment and moral themes related to marriage decline, revealing platform differences and moral influences on public attitudes.
Contribution
It introduces LLM-assisted content analysis applying moral ethics framework to large-scale social media data on marriage in China, uncovering moral and sentiment patterns.
Findings
Weibo posts are more positive than Xiaohongshu posts.
Posts invoking autonomy and community tend to be negative.
Divinity-related posts are generally positive.
Abstract
China's marriage registrations have declined substantially, dropping from 13.47 million couples in 2013 to 6.1 million in 2024. This study examined sentiment and moral elements underlying 219,358 marriage-related posts from Weibo and Xiaohongshu using large language model (LLM)-assisted content analysis. Drawing on Shweder's Big Three moral ethics framework, posts were coded for sentiment (positive, negative, neutral) and moral elements (autonomy, community, divinity). Results revealed platform differences: Weibo leaned toward positive sentiment, while Xiaohongshu was predominantly neutral. Most posts lacked explicit moral framing. However, when moral elements were invoked, significant associations with sentiment emerged. Posts invoking autonomy and community were predominantly negative, whereas divinity-framed posts tended toward positive sentiment. These findings suggest that concerns…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAttachment and Relationship Dynamics · Evolutionary Psychology and Human Behavior · Computational and Text Analysis Methods
