Text mining self-disclosing health information for public health service(无全文)

作者:Ku, Yungchang; Chiu, Chaochang; Zhang, Yulei; Chen, Hsinchun; Su, Handsome 刊名:Journal of the Association for Information Science and Technology 上传者:

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【摘要】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.

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Text Mining Self-Disclosing Health Information for Public Health Service Yungchang Ku Department of Information Management, Yuan Ze University, Chung Li, Taoyuan 32003, Taiwan and Computer Center, Central Police University, Kueishan, Taoyuan 33304, Taiwan. E-mail: ycku1230@gmail.com Chaochang Chiu* Department of Information Management, Yuan Ze University, Chung Li, Taoyuan 32003, Taiwan. E-mail: imchiu@saturn.yzu.edu.tw Yulei Zhang The W. A. Franke College of Business, Northern Arizona University, Flagstaff, AZ 86011, USA. E-mail: yulei.zhang@nau.edu Hsinchun Chen Artificial Intelligence Lab, Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ 85721, USA. E-mail: hchen@eller.arizona.edu Handsome Su Counseling Center, Central Police University, Kueishan, Taoyuan 33304, Taiwan. E-mail: handsome@mail.cpu.edu.tw 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 pro-posed analytical framework, we conducted an experi-mental 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

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