Name
Abstract | ||
|---|---|---|
AbstractUnderstanding 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1002/asi.23025 | Periodicals |
Keywords | Field | DocType |
text mining | Public health,Health care,Data mining,Public health surveillance,Social media,Computer science,Information gain,Knowledge management,Health information | Journal |
Volume | Issue | ISSN |
65 | 5 | 2330-1635 |
Citations | PageRank | References |
1 | 0.36 | 51 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yungchang Ku | 1 | 80 | 5.37 |
| Chaochang Chiu | 2 | 207 | 21.42 |
| Yulei Zhang | 3 | 350 | 25.66 |
| Hsinchun Chen | 4 | 9569 | 813.33 |
| Handsome Su | 5 | 1 | 0.36 |