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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (2): 108-117    DOI: 10.11925/infotech.2096-3467.2018.0619
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Knowledge Discovery of Online Health Communities with Weighted Knowledge Network
Juhua Wu1,Yu Wang1,Ming Li2(),Shaoyun Cai1
1School of Management, Guangdong University of Technology, Guangzhou 510520, China
2The Hospital of Guangdong University of Technology, Guangzhou 510006, China
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[Objective] This paper integrates knowledge from fragmented user message, aiming to identify the needs of online health communities with information extraction and popular topic analysis techniques. [Methods] First, we used the Octopus Collector to retrieve posts from the BHC Forum of 39 Health Net. Then, we applied the weighted knowledge network model to explore the data. Finally, with the help of ICTCLAS 2013, BibExcel and Ucinet packages, we conducted word segmentation, obtained word frequencies, as well as filtered and, visualized the data. [Results] We constructed a user knowledge network and sub-networks for knowledge exchanges, user’s attention and the most popular topics. Combining word frequency and attention identified topics and relationship among them. [Limitations] More research is needed to examine the changing topics of different online health communities, replying posts, and time spans. [Conclusions] This study addresses issues facing the fragmented knowledge, and users’ information needs. It supports website knowledge management and medical diagnosis.

Key wordsOnline Health Communities      Weighted Knowledge Network      Information Requirement      Knowledge Discovery     
Received: 04 June 2018      Published: 27 March 2019

Cite this article:

Juhua Wu,Yu Wang,Ming Li,Shaoyun Cai. Knowledge Discovery of Online Health Communities with Weighted Knowledge Network. Data Analysis and Knowledge Discovery, 2019, 3(2): 108-117.

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