What users seek and share in online diabetes communities : examining similarities and differences in expressions and themes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Original languageEnglish
Pages (from-to)311-331
Journal / PublicationAslib Journal of Information Management
Issue number2
Online published6 Dec 2021
Publication statusPublished - 23 Feb 2022


Purpose - This paper aims to investigate user health information seeking and sharing patterns and content in an online diabetes community and explore the similarities and differences in the ways and themes they expressed. 
Design/methodology/approach - Multiple methods are applied to analyze the expressions and themes that users seek and share based on large-scale text data in an online diabetes community. First, a text classifier using deep learning method is performed based on the expression category this study developed. Second, statistical and social network analyses are used to measure the popularity and compare differences between expressions. Third, topic modeling, manual coding and similarity analysis are used to mining topics and thematic similarity between seeking and sharing threads. 
Findings - There are four different ways users seek and share in online health communities (OHCs) including informational seeking, situational seeking, objective information sharing and experiential information sharing. The results indicate that threads with self-disclosure could receive more replies and attract more users to contribute. This study also examines the 10 topics that were discussed for information seeking and 14 topics for information sharing. They shared three discussion themes: self-management, medication and symptoms. Information about symptoms can be largely matched between seeking and sharing threads while there is less overlap in self-management and medication categories. 
Originality/value - Being different from previous studies that mainly describe one type of health information behavior, this paper analyzes user health information seeking and sharing behaviors in OHCs and investigates whether there is a correspondence or discrepancy between expressions and information users spontaneously seek and share in OHCs.

Research Area(s)

  • Health information seeking, Health information sharing, Online health community, Self-disclosure, Text classification, Topic modeling

Citation Format(s)