I’ve tried all sorts of hobbies over the years. Probably my worst has been building Airfix models. It’s the glue. And the painting. And don’t get me started on those transfer things that are supposed to ‘slide on with ease’ to complete your masterpiece. There are several boxes of ‘bodged’ attempts lurking in our attic. I completely lack the dexterity and patience for that sort of skilled precision work, unlike my oldest son who is astonishingly good at it…

Hello #TiL253…  Fortunately, for the people who appreciate a well-built scale model, over the last 18 months I’ve been occupied by #TiL253 (TiL = Today I Learned, 253 = number of TiL’s aimed for in a calendar year). This is basically me recording the bits and pieces I learnt, or find interesting, using the hashtag of #TiL253 on Twitter. The thinking behind it is all explained here in a blog post: #TiL253 (no longer and experiment).

The long over-due promise of, ‘I’m going to do something with all this information’ has now started in earnest.  Over recent weeks I’ve developed a framework in SenseMaker (some details here) to try to bring things together and see if I can draw out anything useful from my random learnings.

The Good News and OK News.  The good news is that I’ve carefully put 48 #TiL253’s into SenseMaker. The 48 are the first 2 months of 2016, about 19% of the target I was aiming for. So the first observation; I made a good start, which is probably fairly typical of a new hobby (especially one starting in January).

The ‘OK’ News is that I’ve learnt a lot about the framework I’d developed for interpreting my TiL’s in SenseMaker. Basically my framework is not as good as it could be, but that’s OK; I’ve worked out what will work better. In the spirit of ‘safe to fail’ experiments, its cost nothing more than my spare time, which is fine for a hobby. I would also like to think I’ve been consciously following the advice of the Bromford Lab on small iterations, explained in this post ‘What’s the difference between a Test and a Pilot’, neatly summarised in this graphic.

Learning from this test will be plugged into the next iteration of SenseMaker, which I’ll summarise in a follow-up post. In the meantime here is what did surface from testing the first two months of #TiL253.

Numbers are accurate, but not always convincing; Narrative is convincing but not always accurate (Dave Snowden)

When I looked at the TiLs from January and February 2016 I was trying to get a sense of what was important. Did it have a connection or resonance with people? Did it lead to anything useful happening? There were broadly three ways of looking at this:

1. Twitter analytics; impressions, engagements, likes, retweets etc (lots of accurate numbers);
2. Conversations with real people about the TiL (or related topics), this was sort of measurable by following the conversation thread on Twitter; and
3. Things that happened as a result of the TiL, much harder to measure, but can be tracked through conversations (narrative).

My main observations from this:

• The Numbers: By looking at impact via numbers (Twitter analytics) I clearly identified the TiL that had the biggest number of impressions, engagements, likes etc. They were useful or interesting things for me and others:
  • A free course on making sense of health evidence;
  • A link to a font developed to help people with dyslexia;
  • A paper describing the evidence against Evidence Based Management; and
  • The concept of leaving a ‘Digital Exhaust’.
• However, this analysis didn’t really excite me. These were ‘good’ TiL’s (the Twitter analytics numbers confirmed that), but they didn’t seem to have had any real impact as far as I could recognise.
• The Narrative: I then had a look at impact which involved more of a subjective judgement by me. I based this on the sort of conversations the TiL had generated on Twitter (which are helpfully linked to the hashtag in a tread) and other conversations that followed.
• The interesting thing  that emerged was a ‘cluster’ of TiL’s and conversation around a similar topic area; Astroturfing, Fake Social Media Accounts and the Creation of Deliberate Ignorance.
• There was also one TiL that was common with those identified by numbers alone. This was about the concept of Minimum Viable Product, which also linked to a blog post I then wrote, so it might not be a pure example of a TiL.

Astroturfing, Persona Management Software,  the Creation of Deliberate Ignorance and #FAKENEWS! This really excites me. From the ‘What was the impact of the TiL’ scale, there were 3 TiL’s that grabbed my attention. They were about:

• Astroturfing – the creation of false grass-roots sentiment on social media;
• Personal Management Software –  fake social media accounts – the ‘tools’ that help you Astroturf; and
• Agnotology – a process of deliberately creating ignorance.

What was really interesting here were the conversations around the topic. People like Jon Beech (@_jonb), Tamsin Sterling (@TamsinSterling1), Janet Villars (@JanetAvillars) and the ubiquitous @ComplexWales were all involved and contributed something useful to the conversation.

Did we foresee FakeNews? This might be a huge leap of retrospective coherence (making sense of things when you look backwards), but I do wonder if we were actually talking about the rise of Fake News?  This was January/February of 2016, long before the claims of social media manipulation and fake news (deliberately created ignorance) had made it into mainstream discussions. That all came much later on in 2016 mostly around the US Presidential Elections.

As I’ve said this might be an example of over exuberant retrospective coherence by me, but there seems to be a conversation that was emerging, that pointed towards future events? What it has done is spur me on with the next iteration of my #TiL253 testing.

So, What’s the PONT?

1. Doing things in your spare time (a hobby) takes longer than working at it full-time, but it does allow for plenty of reflection and ‘slow thinking’. I like that.
2. There’s a lot of truth in the Dave Snowden quote about Numbers and Narrative. I drew far more useful learning out of the narrative analyis that the ‘accurate’ numbers.
3. The next stage of my test is to get closer integration of the Numbers and Narrative.  I’ve made mistakes and learnt a lot, but surely that’s the point of an experiment.