Article social networks, meaning and redundancy

This is very much a 'thinking aloud' post. In October last year I posted Structure in Trip an article that described the social networks of articles in Trip, based on clickstream data. The analysis allowed me to produce graphs like the one below (based on the clickstream data of people searching for UTI.The structure is clear and I've labelled a few, the most prominent being UTIs and cranberry (in the bottom left of the graph).  I'm increasingly of the opinion that this can be used to speed up the review process and also improve the search experience (but search is for another day). In social network analysis there is a view that within a cluster there is a lot of duplicated information.  If you think about your social networks your close friends probably know lots of the same things as you - this duplicated information/knowledge about birthdays, addresses etc.  I can't help feeling this is likely in clusters of articles.  So, take the cluster of UTI and cranberry there's probably a lot of duplicated information (background information I would have thought).  But there is also lots of unique information (e.g. each set of results will be unique).  Then the conclusions are probably split into three main types - positive, negative, uncertain/ambiguous.So,  as a precursor to more in-depth work I simply took the articles and created a word-cloud (I did do some editing to remove terms I felt were unhelpful):And this is the thinking aloud part - I'...
Source: Liberating the literature - Category: Technology Consultants Source Type: blogs