Last week I posted an article The social networks of articles. In the ten days since then things have moved on with additional further analysis provided by Orgnet, LLC. As an aside look around the site, the case studies are brilliant!
We supplied the same data as last time, a sample of UTI searches (either alone or with additional terms) and what came back was stunning (amazing what you can do with the appropriate expertise and software – InFlow 3.1):
The image above reinforces my initial analysis that there is a rich structure within the data. Each node is a unique article in Trip and the links are made from the clickstream data (see previous post)
But, does the structure have a meaning? That required additional analysis, see annotated image below (click on image to expand):
I don’t think anyone can appreciate how exciting this was for me! Even with a small sample of data we’re revealing a rich structure; a rich structure that has meaning. In effect, by using Trip, our users are curating the content, crowdsourcing the organisation of it.
I’ve had these images for a few days now and I’m still reflecting on the next steps. In keeping with my Clinical Like Me idea I’d be really interested in seeing how networks compare by similar clinicians. So, at a high level the network for UTI would likely be different if the search was from a general/family practitioner versus a urologist versus a paediatrician. But loads of other potential applications from speeding up the review process, highlighting related articles etc.