TY - JOUR
T1 - Unveiling the Making of Trending Topics on a Digital Platform
T2 - A Research Note on Chinese Sina Weibo
AU - Liu, Xiaoyan
AU - Zhao, Jiarui
AU - Li, Zhiyao
AU - Wang, Dan
AU - Chen, Anfan
N1 - The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Fund of China (No. grant ID: 23BXW095) and Start-up grant for New AP-RAP of Hong Kong Baptist University (163102).
Publisher Copyright:
© The Author(s) 2025.
PY - 2025/3/11
Y1 - 2025/3/11
N2 - Understanding the dynamics and duration of trending topics on digital platforms has been deemed a crucial issue of research in computer-mediated communication. Employing event history analysis (EHA), this study attempts to examine the antecedents of the lifespan and evolution of trending topics on Sina Weibo, one of the most prevalent social media platforms in China. Based on a collection of 2,386 Sina Weibo trending topics that emerged between January 1 and January 31, 2022, the study explores how factors of trending topics, especially their semantic and textual features, significantly influence topic persistence. Our findings indicate that the median survival time of Weibo trending topic was 6.28 hours, with an average of 8.29 hours. In addition, exogenous non-viral topics, driven by external events or media coverage, tend to remain on the trending list longer than other topics. Furthermore, political topics tend to have a relatively longer period of survival duration when compared to social events, life records and moods, and fashion and entertainment topics. Lastly, actionable and opinion-oriented topics tend to have shorter longevity compared to informational and emotional topics. By quantifying the factors that affect trending topic duration, the study offers a novel theoretical perspective on the role of political drivers and external influences in shaping collective and connective digital discourses. The findings contribute to the broader field of digital platform communication, particularly in public opinion management and content governance on social media.
AB - Understanding the dynamics and duration of trending topics on digital platforms has been deemed a crucial issue of research in computer-mediated communication. Employing event history analysis (EHA), this study attempts to examine the antecedents of the lifespan and evolution of trending topics on Sina Weibo, one of the most prevalent social media platforms in China. Based on a collection of 2,386 Sina Weibo trending topics that emerged between January 1 and January 31, 2022, the study explores how factors of trending topics, especially their semantic and textual features, significantly influence topic persistence. Our findings indicate that the median survival time of Weibo trending topic was 6.28 hours, with an average of 8.29 hours. In addition, exogenous non-viral topics, driven by external events or media coverage, tend to remain on the trending list longer than other topics. Furthermore, political topics tend to have a relatively longer period of survival duration when compared to social events, life records and moods, and fashion and entertainment topics. Lastly, actionable and opinion-oriented topics tend to have shorter longevity compared to informational and emotional topics. By quantifying the factors that affect trending topic duration, the study offers a novel theoretical perspective on the role of political drivers and external influences in shaping collective and connective digital discourses. The findings contribute to the broader field of digital platform communication, particularly in public opinion management and content governance on social media.
KW - duration
KW - event history analysis
KW - Sina Weibo
KW - social media
KW - trending topics
UR - http://www.scopus.com/inward/record.url?scp=105000452136&partnerID=8YFLogxK
U2 - 10.1177/08944393251324647
DO - 10.1177/08944393251324647
M3 - Journal article
AN - SCOPUS:105000452136
SN - 0894-4393
JO - Social Science Computer Review
JF - Social Science Computer Review
ER -