A social diagnosis mechanism for healthcare knowledge sharing

Lien Fa Lin, Yung Ming Li*, Yen Chen Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In recent years, social networks have grown rapidly, and their applications in the healthcare domain are increasingly proposed. Using the crowd wisdom generated from social networks, we can find similar and reliable people sharing helpful experiences. The existing dedicated social networking services for health mainly focus on sharing, but not categorising and extracting. In this research, we construct an environment for social knowledge sharing and expert referring. Analysing queries from online public health databases and the factors of health similarity, social reliability and social intimacy, we extract health knowledge to recommend relevant social knowledge (also called threads) and helpful experts providing consulting. Specifically, the proposed social diagnosis mechanism helps the health seeker to identify relevant threads and recommends enthusiastic experts for healthcare support. Experimental results reveal that the proposed mechanism can effectively improve healthcare knowledge sharing and realise diagnosis support from the crowd.

Original languageEnglish
JournalJournal of Information Science
StateAccepted/In press - 2023


  • Crowd diagnosis
  • crowd wisdom
  • healthcare
  • knowledge sharing
  • social network


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