Keywords
Xiaohongshu
Social media analysis
User engagement
Sports content
How to Cite
Abstract
This study explores the impact of AIGC-generated sports content on user sentiment and engagement on Xiaohongshu, a Chinese social media platform.Using MediaCrawler, we collected posts and comments related to sports topics generated by AI. Sentiment analysis was performed using SnowNLP to classify comments as positive or negative, with 57.1% showing positive sentiment and 42.9% negative. Despite the prevalence of positive sentiment, no significant difference in engagement (measured by likes) was observed between positive and negative comments.Temporal analysis revealed that user engagement is event-driven, with spikes in positive sentiment during major sports events. Additionally, word cloud analysis highlighted promotional and spam-like themes, suggesting challenges in content moderation. The interaction patterns showed a skewed distribution, with a small number of highly active users dominating discussions.The findings suggest that timing, relevance, and event alignment are more critical than sentiment polarity in driving engagement.The study provides insights for content creators and platforms on optimizing AIGC strategies through trend alignment and effective user collaboration.
References
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv preprint arXiv:2303.04226.
Chen, Z., Sun, W., Tian, Y., Jia, J., Zhang, Z., Wang, J., ... & Zhang, W. (2024). GAIA: Rethinking action quality assessment for AI-generated videos. arXiv preprint arXiv:2406.06087.
Fan, J. (2024). In and against the platform: Navigating precarity for Instagram and Xiaohongshu (Red) influencers. New Media & Society, 14614448241270434.
Feng, Y., Fong, S., Guo, K., & Tan, H. (2024, June). Travel with Xiaohongshu app: The influence of user-generated content in Generation Z's travel expectations. In Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2024), March 29–31, 2024, Wuhan, China.
Hu, Z., Ding, Y., Wu, R., Li, L., Zhang, R., Hu, Y., ... & Fan, C. (2023). Deep learning applications in games: A survey from a data perspective. Applied Intelligence, 53(24), 31129-31164.
Lin, B., & Shen, B. (2023). Study of consumers’ purchase intentions on community e-commerce platforms with the SOR model: A case study of China’s “Xiaohongshu” app. Behavioral Sciences, 13(2), 103.
Sun, Y., & Ly, T. P. (2023). The influence of word-of-web on customers’ purchasing process: The case of Xiaohongshu. Journal of China Tourism Research, 19(2), 221-244.
Tan, J. (2024). A critical research on Xiaohongshu for information sharing for Chinese teenagers. Profesional de la información, 33(1).
Tao, W., Gao, S., & Yuan, Y. (2023). Boundary crossing: An experimental study of individual perceptions toward AIGC. Frontiers in Psychology, 14, 1185880.
Wen, J., Zhang, Y., Chen, Y., Zhong, W., Huang, X., Liu, L., & Niyato, D. (2024). Learning-based big data sharing incentive in mobile AIGC networks. arXiv preprint arXiv:2407.10980.
Xu, M., Du, H., Niyato, D., Kang, J., Xiong, Z., Mao, S., ... & Poor, H. V. (2024). Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services. IEEE Communications Surveys & Tutorials.
Yu, Z. (2025). Xiaohongshu: Modernizing social commerce in China's digital landscape. In Cases on Chinese Unicorns and the Development of Startups (pp. 327-346). IGI Global.
Zhang, Y., & Hu, W. (2024, September). AI-driven creativity unleashed: Exploring the synergistic effects of UGC and AIGC in metaverse gaming from a user perspective. In Proceedings of the 29th International ACM Conference on 3D Web Technology (pp. 1-5).
Zhang, Z., Jianxun, Z. E. N. G., Cuijuan, X. I. A., Dongbo, W. A. N. G., Baiyang, L. I., & Yingchun, C. A. I. (2023). Information resource management researchers' thinking about the opportunities and challenges of AIGC. Journal of Library and Information Sciences in Agriculture, 35(1), 4.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2024 ChunHong Yuan, Xue Wang, Zhimin Yan, Jingyi Tang