Text mining self-disclosing health information for public health service
| Autor: | Yulei Zhang, Hsinchun Chen, Chaochang Chiu, Yungchang Ku, Handsome Su |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
medicine.medical_specialty
Information Systems and Management Knowledge management Computer Networks and Communications Computer science business.industry Public health Library and Information Sciences Public health service Text mining Public health surveillance Health care medicine Social media Health information business Information Systems |
| Zdroj: | Journal of the Association for Information Science and Technology. 65:928-947 |
| ISSN: | 2330-1635 |
| DOI: | 10.1002/asi.23025 |
| Popis: | Understanding specific patterns or knowledge of self-disclosing health information could support public health surveillance and healthcare. This study aimed to develop an analytical framework to identify self-disclosing 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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