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January 2015

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

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):

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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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2014, looking back with pride

At the end of 2013 I did a review of the year and now, in early 2015, I thought I’d repeat the exercise for 2014!

First, the stats:

  • We had over 3,600,000 page views.
  • We are up to registered user number 140,000.  However, the standard view is to discount the number by 10-20% for users who no longer use the account and/or spam accounts.  So, we probably have 115-125,000 registered users.
  • The average duration on the site continues to increase 5.08 minutes compared with 4.11 in 2013. This is mirrored in the number of pages per session, increasing from 3.26 to 3.89
  • The bounce rate (people who just visit one page and then leave without engaging) has decreased by 20%

The above represents an ongoing trend which is seeing less unique users but the ‘quality’ is higher in that the users are more engaged and making better use of the site.  It is this engagement that is so satisfying, much more important than some – ego boosting – headline of number of unique visitors (although 3.6 million page views is quite impressive)!

Financial insecurity has been a recurring theme for Trip and I’m really pleased as I think we’re fine for now and this is based on two facts:

  • We’ve secured a couple of grants recently which help in any number of ways.
  • We’ve finally arrived at a business model (freemium) which we will roll out in March (I hope).  I’m optimistic as we’ll be offering a great premium offering and hopefully a number of users and institutions will sign-up.

At the end of 2013 I reported on the disappointment of missing out on an honorary professorship but I was very pleased to be given an honorary fellowship at the Centre for Evidence-Based Medicine (CEBM) at Oxford University.  The CEBM runs the wonderful Evidence Live series of conferences and I’ll be involved again in the session ‘EBM into Practice: Future of evidence synthesis: a new paradigm’ which will be alongside Carl Heneghan, Martin Burton and Tom Jefferson.

Other bits and bobs from the year:

  • One of the grants was from the EU Horizon 2020 funding and will see me getting involved in lots of interesting research relating to multi-lingual search as well as a big chunk of machine reading and learning, including an overhaul and enhancement to our rapid review system.
  • My role in Public Health Wales (PHW) seems to be working itself out as I was given the role of lead for knowledge mobilisation (a term I dislike) and I’ve just finished a draft strategy on making PHW more ‘evidence-based’.  I believe my role will then move into delivering on the strategy – which should be a nice challenge.
  • I’ve continued to conduct work in the social networks of articles with the huge support of the wonderful Valdis Krebs.  As a little treat I’ve added two images of further analysis below – happy to share more if anyone is interested!

Other than the above there have been so many other things but many are important to me but probably less so to others.

There is also another, really major, project I’m starting to explore but for various reasons I can’t share now.  But it builds on the answer engine concept but there is the potential for Trip to work with a huge commercial partner.

Finally, a very large thank you to:

  • The users, without you Trip would be nothing!
  • Those users that completed the various surveys.
  • The members of the Trip advisory board for being very generous with your time and your collective knowledge/wisdom.
  • The many incredible people who I have interacted with – I really am lucky. 

2014 has been great and I hope – given the reduced financial stress – 2015 will be even better.

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