Previously submitted to: JMIR Diabetes (no longer under consideration since May 09, 2019)
Date Submitted: Apr 12, 2019
Open Peer Review Period: Apr 23, 2019 - Apr 23, 2019
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Semantic Interpretation of the map with Diabetes-Related Websites
Background:
Diabetes as a chronic condition requires continuous medical care and constant patient self-management. Such care and self-management involve several stakeholders in order to improve health outcome and patient quality of life. In our prior work, we used the networks of World Wide Web (WWW or the Web) websites to highlight the connections of some stakeholders involved in diabetes as an organized space filled with communities rather than a randomly organized network. However, some interrogations remain: how to semantically explain the relationships inside the network of diabetes-related websites to explain why some websites are getting closer than others? What do they have in common if they are in the same cluster which was detected by a community detection algorithm?
Objective:
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.
Methods:
To semantically explain the clusters uncovered in the network, we set 6 categories of tags representing different dimensions of the websites’ qualities and topics themselves representative of stakeholders. We tagged each website according to these categories and performed various analysis to better understand the relationship between tagging results and the clusters found by a community detection method. In the end, we applied auto correlation to present the most important tags for computers predicting the clusters.
Results:
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. Some dimensions have mutually exclusive values while others are multivalued. These tags are unequally spread among the websites. In the end, the best 10 tags were selected to help analyzing the corpus and predict the clusters. However, the result shows a very low prediction performance using tags to determine the clusters of diabetes-related websites, except for class 1 and class 2.
Conclusions:
Although this study could not show clearly that the web organization can be explained at the semantic level with the proposed set of tags, the two biggest clusters can be clearly defined by a few specific tags while the others are mixed. 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. If we want to do so, we need either to choose the proper tags to explain the clusters or to extend more diabetes-related websites tagging by refined values. In turn, the tags can be used to analyze the corpus and they shall also be optimized for future broader analysis of the diabetes web space.
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