Documentation, Informaiton & Knowledge ›› 2025, Vol. 42 ›› Issue (3): 145-158.doi: 10.13366/j.dik.2025.03.145

• Knowledge, Learning & Management • Previous Articles    

The Evolutionary Patterns of Group Consensus for Knowledge Co-creation in Online Health Communities

YI Ming, XIONG Yutong, LIU Ming, ZHOU Yang   

  1. School of Information Management, Central China Normal University, Wuhan, 430079
  • Online:2025-05-10 Published:2025-06-23
  • Contact: Correspondence should be addressed to YI Ming, Email: yiming0415@ ccnu.edu.cn, ORCID: 0000-0002-4864-6025
  • Supported by:
    This is an outcome of the Key Project "Research on the Mechanism of Knowledge Co-creation and Guidance Mechanism of Online Health Community"(21ATQ006)supported by National Social Science Foundation of China.

Abstract: [Purpose/Significance] The essence of knowledge co-creation in online health communities(OHCs)lies in the improvement of disease treatment approaches through the gradual enhancement of group cognition and the formation of group consensus. This study aims to reveal the characteristics of group consensus evolution patterns and their key influencing factors at both macro and micro levels, thereby providing a basis for guiding and intervening in the knowledge co-creation process. [Design/Methodology] This study uses a data set comprising 16,822 valid comments extracted from 998 discussion posts in an OHC called "Yuaigongwu", spanning from January 2013 to August 2023. Firstly, to address the concealment of users' standpoint, a group consensus measurement method integrating semantic structure analysis is proposed. Secondly, on this basis, the evolution patterns of group consensus are identified and refined by employing the K-mediods and Dynamic Time Warping(DTW)algorithm. Finally, a predictive model based on the LightGBM algorithm is constructed to analyze and forecast the evolution patterns of group consensus in online health communities. [Findings/Conclusion] The research results show that the evolution patterns of group consensus in knowledge co-creation within OHCs can be abstracted into four categories: Initial Surge-Decay Pattern, Significant Fluctuation Pattern, Swift Surge-Stabilization Pattern, Stage-wise Incremental Pattern. Seven factors, such as negative emotion scores, topic quality, and credibility, have a relatively significant influence on the formation of group consensus evolution patterns in online health communities. Among them, topic quality and social attribute similarity play a particularly prominent role in promoting group consensus growth. For discussion posts with a Initial Surge-Decay Pattern, it is recommended to enhance group consensus by adopting concise topic expressions and encouraging participation from users with similar backgrounds. [Originality/Value] This study introduces a method for measuring group consensus that is applicable to knowledge co-creation in OHCs, and provides new expansion ideas for developing dynamic research on group consensus.

Keywords: Online health communities, Knowledge co-creation, Group consensus, Evolution pattern, Model prediction