Document (#38264)

Author
Ku, Y.
Chiu, C.
Zhang, Y.
Chen, H.
Su, H.
Title
Text mining self-disclosing health information for public health service
Source
Journal of the Association for Information Science and Technology. 65(2014) no.5, S.928-947
Year
2014
Abstract
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.
Field
Medizin

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