Semantic Interpretation of the map with Diabetes-Related Websites

Abstract

Diabetes as a chronic disease requires continuous medical care and constant patient self-management which involve several stakeholders to improve health outcome and patient quality of life. In our prior work, we used the networks of World Wide Web to highlight how stakeholders of diabetes link to each other online. The aim of this study is to use a semantic approach focusing on the diabetes-related websites to better understand the common interest shared by the same clusters which were detected in our previous study of stakeholders on diabetes. To achieve this, we employed the data annotation and machine learning to study which combinations of tags can predict or explain the clusters. In the end, a total of 430 websites which are detected into 5 clusters have been tagged with 38 different tags from 6 different dimensions in this study. Although the result shows a very low prediction performance using tags to determine the clusters of diabetes-related websites, except for cluster 1 and cluster 2, this reflects the community reality: a mix of websites of different types that create a mixed but localized space. It proves the community can have a tagging scheme occasionally but it is still hard to use semantical approach to predict accurately the clusters.