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Liberating the literature

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networks

Search safety net

As we move forward after the introduction of our Premium product we can start to plan future developments and one is a search safety net!

Using our click stream data we can see what articles you’re looking at suggest other documents that you should consider.  Take this network map (click to enlarge):

This is based on searches on Trip for urinary tract infections.  Each blue square (node) represents an article and the lines linking them are created when a user clicks on two (or more) articles in the same search session.

If we have this information we can build a really useful system.  A user comes and does a search of Trip for UTI and finds, from articles in the bottom right of the above image, a number of articles (marked in red in the image below):

It is clear that they may have overlooked four articles (marked as blue nodes) so we alert the user.  It gives them a chance to double check the results.  They may have deliberately excluded them or they may have simply made a mistake.  If it’s the latter then the system will have served its function as a safety net.

I’ve started using the phrase ‘Trip makes finding evidence easy’ but with this technique we could also claim that ‘Trip easily helps you not miss key evidence’.  Not quite as succinct, but you get the picture!

This is a value added service so I envisage it only being available to Premium users of Trip.  Hopefully another reason to upgrade.

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Trip tiles

We’ve been live, as a Freemium service, for a little over two weeks.  In my more pessimistic (pre-launch) moments I was thinking that at this stage I may be having to abandon the whole idea as no-one was purchasing Trip.  However, I’m delighted that this is not the case!  We’re massively ahead of schedule and as such we’re accelerating various upgrades that we’d hoped to do towards the end of 2015.

Even more exciting, we’re thinking of new ideas!

One idea springs from my desire to do something interesting with the Timeline.  The Timeline records your searches and articles viewed on Trip and not much else.  So, one idea is to create something called Trip Tiles!  A fresh tile would be created with every new search and at the top of the tile would be the search terms and underneath would be the articles viewed.  In many ways this is what the timeline currently does.  But I think there’s the potential to link other people’s searches.  So, you might search and find three articles and as part of that process we highlight that 1 or more of the articles has been viewed in someone else’s timeline and offer you the chance to see their tile.

Best illustrate that with one of my legendary attempts at a picture (if we roll out this feature we’ll get them properly designed):

You could go from tile to tile both browsing and looking to see if you’ve missed any useful articles that someone else has already found.  Not only that you can see what search terms they’ve used – again possibly useful.
How we’d implement this would be a challenge, but I’d see that as an interesting challenge not a particularly tough one!  Any feedback on the idea would be appreciated – comments on the blog or via email: jon.brassey@tripdatabase.com

People who looked at this article, also looked at…

In my previous post Ok, I admit it, I’m stuck (a title people seem to really like) I highlighted the difficulty in finding meaning in our clickstream data (the data generated by users interacting with the site).  One thing that I had thought about and a couple of people have subsequently raised is an Amazon style ‘People who looked at this article, also looked at this one..’, a feature I find really interesting and frequently useful.

So, taking some earlier work on mapping UTI data  I started doing further analysis but it was based on this graph.

I started with an article that looked in an interesting place and picked document 2056462 (Cranberry juice/tablets for the prevention of urinary tract infection: Naturally the best? from the publication Tools for Practice 2013) and then followed the links from there.  Some have since been removed or updated.  But, we can say that ‘People who looked at Cranberry juice/tablets for the prevention of urinary tract infection: Naturally the best? also looked at…

  • Novel Concentrated Cranberry Liquid Blend, UTI-STAT With Proantinox, Might Help Prevent Recurrent Urinary Tract Infections in Women (Urology, 2010)
  • Recurrent urinary tract infection and urinary Escherichia coli in women ingesting cranberry juice daily: a randomized controlled trial (Mayo Clinic proceedings, 2012)
  • Cranberry is not effective for the prevention or treatment of urinary tract infections in individuals with spinal cord injury (DARE, 2010)
  • Cranberries for preventing urinary tract infections (Cochrane Database of Systematic Reviews, 2009)
  • Cranberry-containing products for prevention of urinary tract infections in susceptible populations (CRD 2012)
  • A randomized clinical trial to evaluate the preventive effect of cranberry juice (UR65) for patients with recurrent urinary tract infection (Journal of infection and chemotherapy, 2013)
  • Urinary tract infection (lower) – women (NICE Clinical Knowledge Summaries, 2009)

I then, as a way of snowballing, took the last article in the list and did a similar thing, which results in ‘People that looked at Urinary tract infection (lower) – women also looked at…

  • Cranberry juice/tablets for the prevention of urinary tract infection: Naturally the best? (Tools for Practice 2013)
  • Urological infections (European Association of Urology, 2013)
  • Recurrent Urinary Tract Infection (Society of Obstetricians and Gynaecologists of Canada, 2010)
  • A randomized clinical trial to evaluate the preventive effect of cranberry juice (UR65) for patients with recurrent urinary tract infection (Journal of infection and chemotherapy, 2013)
  • Urinary tract infection (lower) – men (NICE Clinical Knowledge Summaries, 2010)

Anyway, I hope it’s clear what’s going on!  On one level it all seems good and interesting in that all the articles seem relevant.  But does it add anything that the initial search wouldn’t have found?  To help I’ve gone through the top list and shown where each of the results appears in the search results (coincidentally the Tools for Practice article came 5th in the results list for a search of urinary tract infection and cranberry):

  • Novel Concentrated Cranberry Liquid Blend, UTI-STAT With Proantinox, Might Help Prevent Recurrent Urinary Tract Infections in Women (Urology, 2010) = Result #38
  • Recurrent urinary tract infection and urinary Escherichia coli in women ingesting cranberry juice daily: a randomized controlled trial (Mayo Clinic proceedings, 2012) = Result #18
  • Cranberry is not effective for the prevention or treatment of urinary tract infections in individuals with spinal cord injury (DARE, 2010) = Result #7
  • Cranberries for preventing urinary tract infections (Cochrane Database of Systematic Reviews, 2009) = Result #14
  • Cranberry-containing products for prevention of urinary tract infections in susceptible populations (CRD 2012) = Result #2
  • A randomized clinical trial to evaluate the preventive effect of cranberry juice (UR65) for patients with recurrent urinary tract infection (Journal of infection and chemotherapy, 2013) = Result #13
  • Urinary tract infection (lower) – women (NICE Clinical Knowledge Summaries, 2009) = Result #54

To me these results are interesting!  The clear ‘outliers’ are the top and bottom results which appeared in result number 38 and 54 respectively.  This is important as it means that they are much less likely to be seen – especially the latter one which would be on the third page of results.

Is this useful?

It will highlight different articles than found from browsing the search results, but is there a cost?  Will users look less at our algorithmic results (the normal results) and rely on these ‘human’ results?  If so, is that good or bad?  I actually think it’ll encourage people to explore more and spend longer on the site – so I don’t think it’ll have a negative consequence.

This is really interesting!

I’m really tempted to open a can of worms by asking if there is any coherence/rationality as to how the linked articles list is generated.  However, as the above list is based on only a sample of data it’d be wrong to place too much weight on things.  Also, even if it is random, so what!?

Finally, I’ve even graphed this out (in not too an appealing way):

Ok, I admit it, I’m stuck

I’ve been talking about article social networks for a while, and last August I wrote ‘Beauty is in the eye of the beholder‘ which contained the image below.

I’ve continued to be fascinated by them and below are two more images – focused on defined areas of the above graph

These are beautiful – but is there more to it?

Both images show definite structure.  So, our users, simply by using the site are adding structure and energy.  I keep getting drawn to the principle of entropy.  I’m absolutely sure that our users are ordering the articles in Trip but does that have any value?

I admit to being relatively clueless – part of the purpose of the post is to see if the wisdom of the Trip users can be brought to bear to try and help me figure out what the above might mean and what might the next steps be!

The above image (taken from Article social networks, meaning and redundancy) shows distinct clusters as well.  In the bottom left is a cluster of articles on UTI and cranberry and it consists of 19 articles.  If you do a search of Trip you find many more than this.  So, our users are not clicking on many articles – so as well as adding structure are they giving us clues as to articles that aren’t worthwhile (based on their collective judgements)?

If you click on one article in that cluster, is it likely that the others will be worthwhile?  What about if a new article is published and joins the cluster based on another person searching and effectively adding the article to the cluster – is that useful?  I’m sure there are no absolutes, but these appear to be hints – surely?

A final thought – the graphs are based on all users.  I imagine the above graph would look different if the user had been a general/family practitioner compared with, say, a urologist.  Stronger clues?

I would be absolutely delighted if anyone can help me figure out the value/meaning of the data.  And, if you can think of ways of working together I’d be delighted to see how we can share the data!

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