Windmills of our Transmedia Minds App

 Posted by on December 13, 2010 at 10:00 pm  Add comments

Doing a bunch of ARG consultancy and while creating lists of user journey options thought I would create this nice little app to help exercise those ‘cross media’ muscles or drive you bonkers. It is a work in progress (what isn’t nowadays) and many more ‘user journey’ options will be added, an auto button and almost done, a genre selector (which adds some story bits). Kind of an auto ARG creator eventually 🙂


  • Choose ‘auto’ (default) and just watch it in veg out mode
  • Choose ‘manual’ then click the top right red button for each of the ‘next’ steps
  • Choose ‘about’ for a little description…

(in progress) A Cross Media User Journey…should describe the route a user takes through a complex multi platform service. It can refer to their thought processes, the actions they take at various points, the story fragments that are revealed at each junction as they travel across a plethora of channels, devices and forms. It often captures the essence of their UX, user experience and is often displayed as a complex chart.

This application, for amusement only, takes us on an endless Alternate Reality Game like User Journey.


 Posted by on August 31, 2010 at 2:58 pm  Add comments

Welcome to my simple multi-platform, mostly commercial, transmedia ‘project’ assignment machine. As I coded this in a couple of hours there are only two sections currently:

  1. TV Extension – create/present a transmedia service, extending the given TV story and meeting the two objectives. Focus on the main objective with the major elements of your cross platform, TV extension and have at least 2 other elements refer to the secondary objective. Use the design chart below to decide on how to expand the story world. Note: currently these are Australian shows, but given 80% of Australian TV is US  or UK imports, they are broad enough for now – but you must be precise in your extension of the existing story
  2. Transmedia – create an original transmedia service (that doesn’t have a film or TV element!) using the given story world ‘suggestion/trigger’ and the two specific objectives. As in 1 focus on the main objective but with this exercise the key is to really develop something self contained and clearly look at the primary narrative existing on other platforms without the luxury of TV or Film. Also the suggested ‘story worlds’ (books and films) are there as generics and basic ‘story environment’ triggers, so for example if it says ‘Jaws’ any shark or sea predator story is acceptable.

Click the tabs attached left and right of the boxes to generate the random suggestions

Please use this image to help you decide on where aspects of the story are best placed

TranSocialMedia Story Telling Workshop Sheet

Mar 192009

Complexity iPhone Camerakit App 22Ever since I joined Twitter (GaryPHayes) I have been fascinated by the subtle ‘etiquette’ of being followed, following and timely updates (as well as the enormous growth and creative potential twitter now affords). It is also interesting watching those traditional media brands and celebrities with a non-twitter and web 2.0 online reputation enter into the fray. What effect do they have? Do they corrupt this young new channel before it has found it’s own feet or is the invasion of old brands and celebs part of its maturation?

Laurel Papworth has far more in-depth coverage of this movement and etiquette across many and various posts on her main blog here but one thing became evident to me as traditional media and celebrities started to ‘infiltate’ Twitter – the instant emergence of old world, short head, long tail distribution. Those brands (individual and companies) already popular in other media on setting up in twitterville started to gain followers like magnets, they swarmed to them – in many cases regardless of what they were tweeting (film and pop stars particularly). We also see old form media channels such as news updates, emerging as useful ‘feeds’ and gaining instant popularity too. Merging with all these are the new stars, traditional bloggers find the transition to micro-blogging easy and so on and so on…

As Twitter has an open API the stats are relatively easy to pull out and there are quite a few sites that do much better analysis than mine below such as TwitterFacts blog, Damon Cortesi and TweetStats. For my little effort below thanks to Twitterholic and its dynamically updated top 1000 (based on followers), I was able to do a quick big picture overview – data taken on the 17 March 2009 !. Before we dig down into the charts themselves a quick high level stat on the Top 1000 tweeters

The top 1000 tweeters have generated 3.45 million tweets and are following 12 million but being followed by 35 million. (note: followers and followings are of course not unique, but the updates/tweets are)

The first chart is what I simply call the  Twitter Long Tail. Starting at the far left with top tweeters CNN Breaking News and Barack Obama at 543k and 486k respectively we move across to the 1000th top tweeter in the world Brad Will with just under 8k followers. I have highlighted a few random tweeters in-between for reference – key thing to note of course is the obvious almost perfect Long Tail shape (I would imagine over time this would smoothe even more – we are still early days)


The highlighted selection here include world renowned bloggers Robert Scoble and Darren Rowse (problogger), passionate artistes Imogen Heap and Stephen Fry, TV getting in on the act Ellen Show and Letterman plus trad media and social media folk. It is interesting for example that The Ellen Show Twitter ID appeared on the 16 March and generated around 200 000 followers off the back of one show – sadly there were only a handful of updates and virtually no following back, a poor user experience – traditional media really needs to make sure it doesn’t corrupt these ‘delicate’ new media channels as it so often does and then tells everyone they don’t really work!

While we are on the global view worth noting that adding all the followers up (thats means each persons follower amount) we end up with 35 million (remember that will contain many duplicates). The point though is to demonstrate the short head’ness here where followers are effectively a ‘rating’ (abstract) of popularity.

Of that 35 million totalled followers

  • 55% are in the top 100
  • 67% are in the top 200 and
  • 85% are in the top 500

To demonstrate this rather spookily smoothe long tail curve I removed the top 50 (that have rather exponentially big figures) and looked at the top 50-500. I started to think also here about the number of updates – do updates bring in followers or is it all about pre-twitter trust and reputation – of course its a to be calculated mix of the two of them – but look below at updates and position…
I went further down this road and looked at the top 100 and their update distribution – the spikes are named. Fascinating again to see that updates do not equal popularity (OK that’s obvious and I will stop labouring that one) but there is a significant high amount of updates going on the in 13-30 areas – remember though we are looking at the creme-de-la-creme of tweeters here and might be too ‘zoomed in’ for meaningful insight?


If your still with me, for reference, here is a quick snapshot of the top 50 World tweeps based purely on following (now you can go and follow them all!). As I keep saying this is not the whole story as we can see – for example CNN following 1 person (is pure broadcast) and Al Gore with only 14 updates (is pure pre-twitter reputation – or 14 amazing world shattering tweets?! – I will go with the former). Of course automated tweeting is rife and there are many in the top thousand who have or are resorting to bots to send messages in their ‘down time’. More after the list…

Some time ago I thought a twitter quotient that took into account updates/followings too is important and the chart below is the same top 1000 tweeters now ordered by a Gary algorithm (made famous on Twitter Agency and Laurel’s post of Australian Journalists on twitter), which changes the landscape significantly. Reproduced from my little contribution to twitter agency here.

Here is a little formula I just cooked up called the Tweet-GQ (Tweet Gary Quotient) that works out a Twitter rating. To be considered as a valuable system to be used on top 100s etc. Before I go into explanation, here is the secret formula

( ((Following/3)+Followers) x (Followers/Updates) ) / 10

This takes into account the raw numbers of followers weighted over following. More importantly it then has an critical multiplier – that of how many updates you do in relation to the followers you generate. So simply, it rewards high numbers of followers but also takes into account how many tweets or updates it took you to get that many followers.

To do this yourself without needing a degree in pure math (or an online calculator – to be done by someone). Here is a simple 3 step DIY version.

  1. Divide followings by 3 and then add this to followers – write the number down
  2. Divide followers by updates – write the number down
  3. Multiply the two numbers above and divide by ten – et voila. Your very own TweetGQ


Finally and while I am on this twitter topic heres a lovely mosaic of 360 out of my current 1300 followers…seems so insignificant now 🙂 But this shows off the power of open API – each of the faces are clickable and therefore followable – is that a word. Bye for now, see you in the twitterverse.

Get your twitter mosaic here.

Get your twitter mosaic here.