Text mining self-disclosing health information for public health service

Yungchang Ku, Chaochang Chiu, Yulei Zhang, Hsinchun Chen, Handsome Su

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Understanding specific patterns or knowledge of selfdisclosing health information could support public health surveillance and healthcare. This study aimed to develop an analytical framework to identify selfdisclosing health information with unusual messages on web forums by leveraging advanced text-mining techniques. To demonstrate the performance of the proposed analytical framework, we conducted an experimental study on 2 major human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) forums in Taiwan. The experimental results show that the classification accuracy increased significantly (up to 83.83%) when using features selected by the information gain technique. The results also show the importance of adopting domain-specific features in analyzing unusual messages on web forums. This study has practical implications for the prevention and support of HIV/AIDS healthcare. For example, public health agencies can re-allocate resources and deliver services to people who need help via social media sites. In addition, individuals can also join a social media site to get better suggestions and support from each other.

Original languageEnglish (US)
Pages (from-to)928-947
Number of pages20
JournalJournal of the Association for Information Science and Technology
Volume65
Issue number5
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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