Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea
- URL: http://arxiv.org/abs/2510.06788v1
- Date: Wed, 08 Oct 2025 09:14:38 GMT
- Title: Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea
- Authors: Zheng Wei, Yunqi Li, Yucheng He, Yuelu Li, Xian Xu, Huamin Qu, Pan Hui, Muzhi Zhou,
- Abstract summary: We analyze 219,127 comments on 668 short videos related to reproduction and parenthood from Douyin and Tiktok in China, South Korea, and Japan.<n>We find that comments focus on childrearing costs in all countries, utility of children, particularly in Japan and South Korea, and individualism, primarily in China.
- Score: 46.334081069868425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Social media use has been shown to be associated with low fertility desires. However, we know little about the discourses surrounding childbirth and parenthood that people consume online. We analyze 219,127 comments on 668 short videos related to reproduction and parenthood from Douyin and Tiktok in China, South Korea, and Japan, a region famous for its extremely low fertility level, to examine the topics and sentiment expressed online. BERTopic model is used to assist thematic analysis, and a large language model QWen is applied to label sentiment. We find that comments focus on childrearing costs in all countries, utility of children, particularly in Japan and South Korea, and individualism, primarily in China. Comments from Douyin exhibit the strongest anti-natalist sentiments, while the Japanese and Korean comments are more neutral. Short video characteristics, such as their stances or account type, significantly influence the responses, alongside regional socioeconomic indicators, including GDP, urbanization, and population sex ratio. This work provides one of the first comprehensive analyses of online discourses on family formation via popular algorithm-fed video sharing platforms in regions experiencing low fertility rates, making a valuable contribution to our understanding of the spread of family values online.
Related papers
- CURVE: A Benchmark for Cultural and Multilingual Long Video Reasoning [58.73855961335903]
CURVE (Cultural Understanding and Reasoning in Video Evaluation) is a challenging benchmark for multicultural and multilingual video reasoning.<n>It comprises high-quality, entirely human-generated annotations from diverse, region-specific cultural videos across 18 global locales.<n>Our evaluations reveal that SoTA Video-LLMs struggle significantly, performing substantially below human-level accuracy.
arXiv Detail & Related papers (2026-01-15T18:15:06Z) - Cross-Platform Short-Video Diplomacy: Topic and Sentiment Analysis of China-US Relations on Douyin and TikTok [53.79007551410356]
We examine discussions surrounding China-U.S. relations on the Chinese and American social media platforms textitDouyin and textitTikTok.<n>This study analyzed 4,040 videos and 338,209 user comments to assess the public discussions and sentiments on social media regarding China-U.S. relations.
arXiv Detail & Related papers (2025-10-25T19:28:58Z) - Measuring South Asian Biases in Large Language Models [1.5903891569492878]
This work addresses gaps by conducting a multilingual and intersectional analysis of Large Language Models (LLMs)<n>We construct a culturally grounded bias lexicon capturing previously unexplored intersectional dimensions including gender, religion, marital status, and number of children.<n>We evaluate two self-debiasing strategies to measure their effectiveness in reducing culturally specific bias in Indo-Aryan and Dravidian languages.
arXiv Detail & Related papers (2025-05-24T02:18:17Z) - Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers [0.0]
We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis.<n>We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories.
arXiv Detail & Related papers (2025-01-09T18:30:14Z) - HOTVCOM: Generating Buzzworthy Comments for Videos [49.39846630199698]
This study introduces textscHotVCom, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments.
We also present the textttComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset.
arXiv Detail & Related papers (2024-09-23T16:45:13Z) - Unveiling Local Patterns of Child Pornography Consumption in France
using Tor [0.6749750044497731]
We analyze local patterns of child pornography consumption across 1341 French communes in 20 metropolitan regions of France using fine-grained mobile traffic data of Tor network-related web services.
We estimate that approx. 0.08 % of Tor mobile download traffic observed in France is linked to the consumption of child sexual abuse materials by correlating it with local-level temporal porn consumption patterns.
arXiv Detail & Related papers (2023-10-17T09:31:26Z) - The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - Mapping Language Literacy At Scale: A Case Study on Facebook [6.402634424631123]
This work systematically studies the language literacy skills of online populations for more than 160 countries and regions across the world.
We develop a population-level literacy estimate for the online population based on aggregated and de-identified public posts written by adult Facebook users globally.
We found that, on Facebook, women collectively show higher language literacy than men in many countries, but substantial gaps remain in Africa and Asia.
arXiv Detail & Related papers (2023-03-21T20:24:13Z) - Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
Country image has a profound influence on international relations and economic development.
In the worldwide outbreak of COVID-19, countries and their people display different reactions.
In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
arXiv Detail & Related papers (2020-09-12T15:54:51Z) - Towards Controllable Biases in Language Generation [87.89632038677912]
We develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups.
We analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics.
arXiv Detail & Related papers (2020-05-01T08:25:11Z) - Quantifying Community Characteristics of Maternal Mortality Using Social
Media [12.265295793821931]
We examine the role that social media language can play in providing insights into community characteristics.
We find that rates of mentioning pregnancy-related topics on Twitter predicts maternal mortality rates with higher accuracy than standard socioeconomic and risk variables.
We then investigate psychological dimensions of community language, finding the use of less trustful, more stressed, and more negative affective language is significantly associated with higher mortality rates.
arXiv Detail & Related papers (2020-04-14T04:57:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.