Exploring the Dynamic Interplay of User Characteristics and Topic Influence on Weibo: A Comprehensive Analysis

Guangce Ruan, Lei Xia (), Xin Wen and Yinuo Dong
Additional contact information
Guangce Ruan: East China Normal University
Lei Xia: Reading Promotion Department, Shanghai Library
Xin Wen: East China Normal University
Yinuo Dong: East China Normal University

Journal of the Knowledge Economy, 2025, vol. 16, issue 1, No 107, 3030-3057

Abstract: Abstract In the contemporary digital landscape, Weibo has emerged as a dominant force, profoundly shaping societal dynamics and communication paradigms. This study investigates the intricate interplay between user characteristics and topic influence on Weibo, addressing a critical gap in existing research. Leveraging cutting-edge techniques such as user group segmentation, Dynamic Topic Modeling (DTM), and Jensen-Shannon divergence, our research delves into the evolving landscape of Weibo users. The study begins by highlighting the staggering growth of Weibo, with 462 million monthly active users and 130 million daily text exchanges, emphasizing the need for updated insights since 2018. Traditional research in this domain has focused on personalized recommendations, sentiment analysis, and user behavior, often relying on keyword extraction. However, this study challenges conventional approaches by thoroughly analyzing attribute features, interaction dynamics, and content, classifying Weibo users into distinct groups based on nuanced characteristics. A novel taxonomy for interest extraction is introduced, considering the temporal dimension and assessing the impact of various topics on Weibo coverage and user activity. This comprehensive approach not only enhances our understanding of Weibo users but also provides a framework for future research in digital communication platforms. Theoretical implications are profound, as this study reveals the existence of specific user typologies with evolving thematic preferences, challenging the traditional notion of categorizing users as a single group. Managerial insights benefit social media administrators, content creators, and marketers equally significantly. By identifying user groups and their changing interests, administrators can enhance user experiences, while content creators and marketers can tailor their strategies for more effective communication. This study unravels concealed patterns in Weibo user groups, offering valuable insights for navigating the ever-changing landscape of online communities, contributing to academic understanding, and providing practical guidance for managing diverse user communities on social media platforms like Weibo.

Keywords: User characteristics; Topic influence; Dynamic analysis; Social media; Online communities; Thematic preferences; User behavior; Digital communication; Social media management; Content customization; User typologies (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13132-024-02021-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-02021-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13132

DOI: 10.1007/s13132-024-02021-9

Access Statistics for this article

Journal of the Knowledge Economy is currently edited by Elias G. Carayannis

More articles in Journal of the Knowledge Economy from Springer, Portland International Center for Management of Engineering and Technology (PICMET)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().