Ku, Y.; Chiu, C.; Zhang, Y.; Chen, H.; Su, H.: Text mining self-disclosing health information for public health service (2014)
0.02
0.015055953 = product of:
0.045167856 = sum of:
0.045167856 = weight(_text_:resources in 1262) [ClassicSimilarity], result of:
0.045167856 = score(doc=1262,freq=2.0), product of:
0.18665522 = queryWeight, product of:
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.051133685 = queryNorm
0.2419855 = fieldWeight in 1262, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.046875 = fieldNorm(doc=1262)
0.33333334 = coord(1/3)
- 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.
