@article{Chen2026, 
author = {Hui Chen and Zhengze Li and Xiaoming Fu},
title = {From Short Videos to Global Learning: A Data-Driven Analysis of the Ecosystem of Chinese Language Learning on TikTok},
year = {2026},
journal = {Journal of Social Computing},
volume = {7},
number = {3},
pages = {215-226},
keywords = {TikTok, Chinese language learning, big data, user behavior analysis, economic and cultural factors, global communication},
url = {https://www.sciopen.com/article/10.23919/JSC.2026.0015},
doi = {10.23919/JSC.2026.0015},
abstract = {This paper presents a data-driven analysis of 75 188 TikTok videos to characterize the global ecosystem of Chinese language learning. We incorporate sentiment analysis and cross-national comparative modeling to reveal that content is defined by brevity, interactivity, and entertainment orientation, forming a globally diffused yet regionally clustered ecosystem. Creators from neighboring countries and major economies drive content production, reflecting the interplay of linguistic proximity, digital access, and cultural affinity. User comments reveal a risk-diluted ecology: The absence of formal assessment encourages more active participation and less formal, more spontaneous interaction. Emotional reactions are primarily driven by the video’s topic and style, rather than its duration. Cross-national analysis further shows that economic scale, trade linkage, and geographic proximity significantly shape both content productivity and audience engagement. These findings advance understanding of algorithmically mediated language learning, offering a framework for analyzing the socio-technical dynamics of digital knowledge exchange in the short-video era.}
}