Health-related hot topic detection in online communities using text clustering

Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities...

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Bibliographic Details
Published in:PloS one 2013-02, Vol.8 (2), p.e56221-e56221
Main Authors: Lu, Yingjie, Zhang, Pengzhu, Liu, Jingfang, Li, Jia, Deng, Shasha
Format: Article
Language:English
Subjects:
Analysis
Biology
Boards
Breast cancer
Cancer
Chronic illnesses
Cluster Analysis
Clustering
Communication
Community participation
Complications
Computer Science
Diabetes mellitus
Digital media
Drugs
Health
Health care
Humans
Internet
Internet - statistics & numerical data
Lung cancer
Lung diseases
Medicine
Patients
Self-Help Groups - statistics & numerical data
Social and Behavioral Sciences
Social media
Social networks
Social Support
Statistical analysis
Studies
Web sites
Websites
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Summary:Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients' needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0056221