#Raspberry Pi #Raspberry Jam meetup in London –

I’ve bought my son a Raspberry Pi for Xmas. Don’t tell him!

The main reason I’m keen to get him interested in this is that I think that we are increasingly being divided into a role of producers and consumers of digital services. It’s about empowerment.

I’d argue that the definition of ’empowerment’ is laden with unspoken social-class bugs (or features, depending on your social class viewpoint).

To my mind, this is a profoundly political issue, but I’ll let Eben Upton and Theo Blackwell explain why this matters much more efficiently than I can here.

Yesterday morning, I was a bit worried. I wanted to take my son to a meetup where he can be enthused about the possibilities offered by his Raspberry Pi.

I want to strike while the iron is hot after he’s opened his presents. I also like the idea of involving young people in this kind of thing. Earlier this year, I organised an Open Data Day for school pupils in the London Borough that I live in. I wanted to encourage them to get under the bonnet of government data. More about that here if you’re interested.

I asked, on twitter, if anyone knew of any #Raspberryjam meetups soon after Xmas in or around London. There aren’t weren’t any planned, but my Twitter feed convened one in no time at all.

John Bevan of Mozilla (the people behind Firefox) ponied up an offer of a central London venue for a meeting on Thursday 3rd January, and Rob Bishop of Raspberry Pi quickly followed with an offer to attend and help out. A few others with experience in running ‘Raspberry Jam’ events also volunteered to help out, and …. bingo! We’ve got a plan.

So: Book your tickets now! And please please please only book a ticket if you’re going to come. And if you change your mind after booking, please take the time to log back into Eventbrite and give your ticket back.

Update: I’ve decided to charge £5 to people who book but don’t turn up. You pay £5 to get the ticket and we give you a £5 note when you arrive. Unfortunately there’s a booking fee that we can’t refund but it’s <£1.

I suspect demand will massively outstrip supply, so please bear this in mind.


Political Innovation in 2012 – what we’ve been up to

Memeserver’s main contribution to the commons in 2012 has been the organisation of the following informal and conversational events in London.

Crowdsourcing analysis for policymakers

How open data is being used government, how it could be used as a participative tool, and what the opportunities / pitfalls could be.

Presented by Andrew Stott

Co-design and policymaking

A look at the practicalities of involving large numbers of people in planning and designing policies, followed by a discussion of the politics and the ethics of ‘collaborative authoring’

Presented by Steph Gray

Policymaking in the cloud

A discussion of new ways of doing things that arise from more dispersed technical networks. ‘Scrum’ project management, open source development and, Peer-to-Peer organisation have all been held up as being ideas that politicians and governments can learn from.

Presented by Dr Andy Williamson

Quicker, cheaper and easier than polls

In the past, opinion polls and focus groups have had a great deal of influence over policymakers. Today, the social media ‘firehose’ provides us with a torrent of opinion and sentiment to draw from. How is this done? And how well does this commercial practice apply to policymaking?

Presented by Dr Nick Buckley

What policymakers can learn from gaming

Technological entrepreneurs have become adept at finding new ways of motivating people, not only managing to change their behaviours but also encouraging them to develop skills, mentor others and solve problems. What can politicians learn from the most successful interactive-content industry the planet has known?

Presented by Jude Ower

Are we too irrational to participate in policymaking?

Can a government that adopts approaches from behavioural economics be trusted to be serious about any kind of participative politics? Are the two oil-and-water opposites? Or is it more complicated than that?

Presented by Warren Hatter

In other developments, we have been working on an ‘open data for schools’ project, and the Political Innovation approach to understanding policymaking in the digital age has formed the basis of a number of one-day professional training events.

Schools and Open Data

Over the past year or so, I’ve been working – on and off – to gather interest in the idea of encouraging school pupils to explore open data, data visualisation, and the political lessons that can be drawn from this.

I won’t go into too much detail about his now as I’ve covered it extensively on another blog with this list of posts. In summary, though, the educational possibilities are very interesting, but I would suggest that developing this idea is an almost essential step for any polity that wishes to engage large numbers of people in rational policy-making processes.

What’s the next step? Well, I’ve pulled together a draft report here, based upon experiences gathered organising a Schools Data Day in association with The London Borough of Barnet and Deloitte LLP, and there are a range of pupil presentations that came out of the hack day that I’m planning to show in some presentations shortly.

I’m hoping to be able to gather enough interest to do a bigger one of these shortly.

Circles of influence

This article on the Pinboard blog comes with the most credible of endorsements: At least four people that I know (who aren’t connected to each other as far as I know) have linked to it on Facebook/Google+/Twitter. In an odd way, the way that I found it shows the other side of the question that the article raises.

Firstly, I completely buy the argument that it advances: That the notion of the ‘social graph’ is a very limited one and it is stuck at the limit to which we are all machine-readable as individuals. On the other hand, I felt compelled to read it because a few influential people within my social graph said it was worthwhile.

Yet, for all of the shortcomings of the concept of ‘social graph’ as a means of mapping general relationships, particularly for marketing purposes…

“Google, for example, uses XFN as part of their Social Graph API. This defines a set of about twenty allowed relationships. (Facebook has a much more austere set: close_friendsacquaintancesrestricted, and the weaselly user_created).

But these common relationships turn out to be kind of slippery. To use XFN as my example, how do I decide if my cubicle mate is a friendacquaintance or just a contact? And if I call him my friend, should I interpret that in the northern California sense, or in in some kind of universal sense of friendship?”

… it’s still a useful heuristic for the narrower purposes of understanding personal influence. We need to first understand what it is to understand what social media tools are trying to achieve. They are trying to find ways in which they can make our relationships more machine readable. There is a lot of effort coming from social media platforms at Google+ and Facebook with a view to monetising our social graph.

So the Pinboard blog has identified a large hole in the strategies of Facebook and Google+. But unless you’re a shareholder, why should we bother about this?

I’d say we shouldn’t. What is of interest, though, is how we can use the tools that we’re getting for free (!) to achieve things.

In my line of work, the big question is how we understand (and exercise) influence. I think we can learn something about this from the concept of the social graph. Remember, at the start of this post, I said why I’d read that Pinboard article? Four people who influence me all linked to it. I think it was four. I can only name two now. But I’d noted the link, at that’s the important thing. It may even be the case that one of the people who linked to it is someone for whom I’d generally ignore their material. But that doesn’t matter.

I’m pretty sure that politicians and journalists respond to ideas and concepts in the same way.

So how does the concept of the social graph help here? For example, this is an old-ish app Facebook: The Friend Wheel. I’ve highlighted friend Dominic Campbell at random here, simply to illustrate that he knows a lot of people that I know (52) pm Facebook.

My personal friend-wheel. Click to enlarge.

This shows all of my Facebook friends and who is connected to who. On the top left-ish end, you’ll see people who are densely connected to each other – often with me as the connector. On the bottom right-ish, a lot of people who barely know anyone else from my circle.

Then I pulled up another old Facebook app – this time, Friend Sets. Using this, I picked five friends who I’ve got to know at different times of my life to see how they connect to each other:

I’ve picked these names at random, and I’m using a free app here. With a better, three-dimensional one, I’d be able to identify all of the different social circles I’m in. Personally, I’m connected to circles that broadly consist of….

  • family and their friends
  • old school / college friends, childhood friends, etc
  • people who I live/ have lived near, or shared a flat with
  • people I used to work with in different jobs & some of their friends
  • fellow UK Labour Party supporters
  • people I know from a village in Ireland that I visit regularly
  • people who are in my political ‘cell’ (humanist, internationalist, democratic-lefty, pro-EU) and their friends
  • people I’ve met in Northern Ireland’s political circles (I’ve taken an interest over recent years)
  • people who are active thinkers around social media and local/central government
  • people who were active in UK political blogging (2005-9 mainly)
  • people with a general interest in new media and politics
  • a very small handful of random unconnected people who don’t fit anywhere else (holidays etc)

The venn-diagram of that lot has a few crossovers. There are one or two interesting surprises in this (“How come you two know each other??”) LinkedIn is often even more surprising in this respect, but less generally interesting. And within those crossovers, there are often people who are well-respected in more than one sphere. . But as the Pinboard blog article says, this intelligence tells us very little that is useful about the thousands of two-way relationships that this represents.

I mention all of this as a prelude to a question I’m going to write about shortly: People may not have a useful social graph, but is there such a thing as a conversational graph?

I think that there is, and that it is a very valuable thing if we can identify it. I’ll be back with a posting on this shortly.

Being a ‘firehose predator’ – looking for changes

A few weeks ago, I posted here on ‘drinking from the firehose‘, looking at what we can learn from the huge tide of commentary that social media is creating for the first time.

For me, the most interesting aspect of this is that it gives journalists, PRs, or even more sophisticated intelligence gatherers, the chance to see things through the filter of the Internet’s hive mind. Journalists, PRs and researchers can learn from Hedge Funds and doctors here:

The interesting information can be found in changes and in the related stories that are spinning around a term that you are watching. A significant shift in the computer-tracked sentiment is the thing that tells the story. Hedge Funds have jumped on Twitter precisely because of this. So have doctors looking for ‘flu epidemics.

In the wild, predators notice movement. The market of human interest is the same. We process newness and change.

To understand a developing situation, the firehose (with the right analysis) can tell us when a new dimension to a story emerges or attitudes to an existing aspect of the story changes noticeably. As predators know, we need to be looking at the space where change is happening. Large numbers are often less important that sharp variations.

Case study: Noticing that a story is happening and what the key factors are

I’d now like to start to look at how we can dig into a story – in stages. I’m doing some work with some developers on a tool called Repknight, and they are aiming to create one-click ways of doing a lot of what follows, but this post is intended as a bit of a slo-mo walkthrough what is possible.

Once we have all of that data in one place (providing we have the processing power – i.e loads of servers), we can start to run all kinds of analysis over what we have. Sentiment analysis is one of the most common processes that we can use to sift this data.

Take a search term. For illustration purposes, I’ll use the recent ‘Dale Farm’ evictions as an example of a developing story. The term ‘dalefarm’ was a text-string that was being used by critics and supporters in mentions of the evictions on social media outlets.

Mining online comments with ‘dalefarm’ in them was, therefore, a useful way of keeping tabs on a developing situation.

So what could we find out?

Let’s start at the most obvious level: Can we can tell if something is happening at all? Here is a graph from Repknight showing mentions of this term across a wide range of social media platforms (Facebook, blogs, Twitter, YouTube, Flickr etc)

Repknight graph showing occurence of the term 'dalefarm'

Yes. We can safely say that on the 18th and 19th October, something was definately happening. While Dale Farm may not be the best example of this (it was a ubiquitous news story that we all knew was going to happen), if you are monitoring a particular term (your organisation’s brand or name, a particular news issue, etc), this can be useful. Journalists, in particular, are looking for relatively large prolonged jumps (a ‘spike’ can often be a story that doesn’t have legs – a rumour that is quickly scotched).

Next, we need to drill into what was happening on those days during a short time-period around this term. We can look at a list of other words that also appear in comments with ‘dalefarm’ in them – like this one (click to enlarge).

Repknight dalefarm sentiment

Here we see a map of the protagonists around this story – the police, Basildon Council, Richard Howitt (an MEP who got involved), bailiffs and, bizarrely, Newts. Each player in the story has some positives and negative sentimented comments around the story on a range of different media platforms.

So, what use is this to us? Sentiment Analysis in itself, isn’t hugely accurate as a quick play with this entry-level tool will show you. As a way of understanding individual short messages, it is deeply flawed.

But automated sentiment analysis is getting better (and people are often bad at detecting sentiment accurately as well!). Changes here are what matters – as investment analysts and epidemic-tracking medics will tell you.

I noticed towards the end of last week that the sentiment around the ‘Occupy London Stock Exchange’ story (#OccupyLSX) was going from a fairly positive general response to quite a negative one. I’d already registered that there was a developing fuss around access to the St Paul’s but I’d tuned it out, thinking it was one of those aspects of the stories that reporters were talking about to fill in time.

But drilling in to that day’s sentiment, I found that ‘St Pauls’ had a strongly negative balance. This wasn’t going to go away. Throughout the day, this grew as the closure of the Cathedral was picked up by opponents as well as neutrals, it became the aspect of the story that dominated the news bulletins.

This information is only one of the building blocks needed to cover a story. All of the information I’ve mentioned here so far is available within a couple of clicks. Repknight are allowing users to build up a store of data around particular subjects over a long period of time, thereby allowing users to be able to identify significant changes properly.

In a subsequent post, I’m going to look at how we can dig into the negative messages, identify the communities that are talking among themselves, identify the influencers within those communities and the connectors between them. We can see who is making the running on a particular story – and even intervene with them to influence how a story unfolds.



Things people in PR & Campaigns need to know: Reading Twitter’s firehose

I’ve recently posted here about how most major social media applications are a bit like an iceberg – and how most of us only get to see the tip of it. I’ve also looked at how the internet could be looked at as a vast conspiracy to make us all machine readable.

The Firehose. Fancy a drink?

Today, I’d like to focus on how we can start to intelligently mine that huge firehose of information and extract useful, meaningful information from it.

This is particularly exciting for us because it’s information that we’ve never been able to see before – a huge torrent of observation, comment and hard data that is being produced in a machine-readable format for the first time ever.

To put this into context, take any point in recent history and imagine you could get millions of people to come to you and volunteer opinions or interesting extracts from the things they’re read or seen in a way that we could tabulate it, weigh it for value and get information from it.

On a slightly darker note, imagine that they do this in a fairly guileless way – often even putting info into our hands that we could exploit or abuse.

Imagine what the Stasi could have done with it? Imagine what benign forces of law-and-order could do with it in order to undermine crime or terrorism? Pop your ‘Liberal’ thinking cap and and spot the potential dangers here. Then try on your ‘sales and marketing’ cap and look at the opportunities or your ‘consumerist’ eyes to see the threats here.
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Things people in PR & Campaigns need to know: No1: Be ‘Machine Readable’

Do you have a scanner in your office? If you do, and you look at the disks that came with it, you’ll probably find that it has an OCR function that you can use.

Napster: It dragged music into the machine.

OCR – Optical Character Recognition – can take old-media (in this case, print) and turn it into digital media. I can scan that pamphlet that I wrote in the mid-1990s and start cutting-and-pasting bits onto various sites I manage, or emailing bits to people who should have read it at the time, dammit!

We’re gradually making print ‘machine readable’ in this way, like Project Guttenberg or Google Books has done. And before the music-on-demand service Spotify came along, Napster galvanised legions of music-pirates to get music digitised and available – by encouraging users to rip their CD collection and share it.

Call Shazam from your phone and it will match music you’re listening to with it’s digitised database. It will text you the song title. Nearly ten years on, this is still the most startlingly cool thing I’ve seen digital media do.
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The Twitter ‘Firehose’: Going beyond the tip-of-the-iceberg.

I’ll be posting up a bit more about social media analytics over the coming weeks and months, but today’s news – ‘Hedge fund uses Twitter sentiment analysis to guide decisions’ – provides a useful opportunity to provide a timely taster.

Twitter: We only see the tip of the iceberg
For example, can we use this kind of approach to improve campaigning, media-monitoring, governance or public health? Because if we find a way of really seeing what is being said on social media platforms, we can find out all kinds of timely information that we wouldn’t always know about. Information that – until recently – even money couldn’t buy.

This changes the very nature of decision-making – and by implication, of government and governance. Governments can even foresee revolutions if they know where to look for them.

I spend a lot of time training clients in the use of social media channels and how they can be used to communicate – in the fullest sense of the word. One of the earliest points that I make to users who have a reasonable amount of experience with Facebook or Twitter, for example, is that these media are not as friendly as they may look.
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More on viral imagery

A while ago, I posted here on a list of images that had gone viral – images that had properties that made people want to forward them on to others.

Here’s a good post with another load of them – including this one (perhaps the most mild mannered one of all of them, but my favourite):

An email with one of those images (and perhaps other messages beside them) is very likely to spread. If it’s a web-link, it can be an image with advertising placed beside it, thereby becoming a powerful bit of marketing.
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