@tldrlit

Reading is hard. It takes time. Time is difficult to come by when life is filled with tweeting and snapchatting. Yet often there’s that nagging feeling that one should be ‘better read’. There are numerous books that we feel we ought to have read, if only maintain an erudite facade at our next cocktail party round at Gideon’s house.

tl;dr;lit attempts to address this problem.

This bot takes works of literature and algorithmically summarizes them, a chapter at a time, to 1% of their original length. These are then read aloud by the lovely voice of Fiona, a Scottish speech synth, and posted at on Twitter at convenient 3 hour intervals. This way entire works of literature can be consumed in bite-sized algo-chunks, giving you the gist of the book, without any troublesome cause to actually ‘read’ or ‘understand’ it at all…

Fiona is currently reading 1984 by George Orwell.

I have Moby Dick, Pride & Prejudice and 50 Shades of Grey lined up, but feel free to suggest more via @erocdrahs

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@algobola II

Algobola was an experiment investigating social contagion. Using twitter as a propagation channel, I introduced the ‘virus’ into the network, using myself as patient zero. I started the infection at 13:00GMT on 28th October 2014.

I knew from the start that there was a danger Twitter would close it down, but I didn’t expect it to happen so soon. Ironically, it appears my bot was automatically flagged and restricted by one of Twitter’s own internal bots – my bot was caught by a bot-policeman…

However, before that happened it managed to expose over 900 people to the virus, each of them being notified via a personalised notice informing them of their changing status as their infection developed.

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The parameters were modelled on Ebola, but modified to take into account the limited attention span of social media users. Once infected, the subject remained infectious to others for 72 hours. At that point, they either survived or died (30% survival rate). Twitter restricted posting rights of the account after about 70 hours, just before the first subject (me) died.

However, even though I could no longer inform the victims, I could still simulate the infection and record the way it propagated through the network. Indeed I continued until Twitter completely stopped API access for the account on 4th November. By this point, 5230 users were exposed.

What emerged is a fascinating chart of social media connections.

I’ve made a visualisaton of some of the data I collected. Each dot represents a twitter user, and the connections between them indicate the vector of infection. Click ‘start’ to cycle through the first 120 hours of infection, or use the buttons to jump to a specific day. If you hover your mouse over a dot it will give you the name of the twitter user.

Click here to launch the interactive view.

Maybe you can find yourself in there.


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What emerges is a rapidly exploding map of social interactions. It gives a quick visual representation of the different kinds of social media users – those who communicate with a select few, and those with a larger network of contacts. It exposes the interrelatedness of the twitter users – who their friends are, how often they communicate – all derived from a very simple analysis of the ‘metadata’.

This stuff is sexy to both data scientists and governments. Which government wouldn’t want to harvest this data? As we live our lives on a connected, easily monitored infrastructure, these kinds of data become a convenient shortcut to our identity as individuals. To all intents and purposes we are our data. These kinds of data represent who we are. We are packets of data, flung into the ether, to be collated and analysed by giant server farms in hidden locations.

Once the data is collated, it is algorithmically analysed. A digital report card is produced, and based on the desires of the enquiring party, ‘persons of interest’ are identified. Sometimes these profiles are produced by marketing companies, like Facebook, hoping to sell ever more granular descriptions of us to entities that wish to advertise to us. Sometimes these profiles are produced by government agencies, hoping to identify individuals as subjects of ‘interest’.

In both cases, the raw data is the same. The data itself is benign until it is interpreted. It’s the algorithmic questions posed of it that produce the representations that humans actually use to make decisions. This recasting of information is possible because of the computational power available, it is necessary because the human mind is incapable of extracting inference from datasets of this size. An awful lot of trust is being put into this inscrutable algorithmic perception, and the track record in this area is not good.

The real issue is one of ethics – Do we want our governments to do this? Is there any evidence that it makes us ‘safer’? How does a legal system deal with humans rendered as data? Do you have the same rights over our digital selves? What is the relationship between the data-self and the real-physical self?

I shall be speaking about these ideas and more in Brighton next week.

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…made this for…

The internet, and especially the device we keep in our pocket, has been blamed for destroying our attention and stealing our creative impulses. “Put that device down, and go outside and play”, we implore our offspring, whilst tapping and swiping our own lives away.

However, just because we’re tapping a screen, does not mean we cannot create. Indeed social media, with it’s ‘direct connection to celebrity’ has spawned a particular form of fan art which is a direct result of these interactions. Fans make fan art, be it drawings, vines, videos or digital collages and desperately tweet them at their idols in the hope of connection. The absurdity of this phenomenon has been noted before, but to my knowledge no one has taken an automated approach to gathering these artworks.

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imadethisfor.tumblr.com is a tumblr created by scanning twitter for the phrase ‘made this for’ and posting any media it finds. The majority of the posts are of fan art, however it also captures more intimate connections between individuals, which are equally fascinating in their own way.

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We are witnessing a new kind of folk art, born of the (perceived) breakdown of communication hierarchies between fan and star. Personally, I find this fascinating, perhaps you do to.


Visit the tumblr


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Silicon Beach 2014

Here I am talking about Guerrilla Epistemology at Silicon Beach earlier this year.

Shardcore sharing at Silicon Beach 2014 from Wise Old Uncle on Vimeo.

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@algobola


disclaimer

Ebola is a serious business, people are dying. The best way to stop the spread of a disease is to contain it at source. There are many organisations actively involved in treating people in West Africa. Do your bit Рlobby your politicians and shake them out of their apathy, or make a donation РI suggest the wonderful M̩decins Sans Fronti̬res as one such organisation worthy of your support.



exposed

Algobola is an investigation into social contagion.

Algobola infects Twitter, it is passed through the exchange of ‘social media fluids’ – in this case, the use of @ mentions. It’s an experiment to see how far a ‘social virus’ can travel, and whether its presence can have any effect on behaviour.

For the purposes of this experiment, I am patient zero – infectious to anyone I mention in my twitter feed (sorry friends). Once someone is exposed, they have a 50% chance of being infected. If they become infected, they are also contagious. There is a 30% chance of survival.

Changes in infectious status are sent directly to the affected user in the form of a modified avatar image.

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Here’s a chart of some test data:


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The number of infected people varies over time, depending on how promiscuous people are in their social network – to some extent it also reflects the day/night posting cycles of the infected population. This test had a 50/50 survival rate. The infection I’ve just started has only a 30% survival rate, so expect more death.

Infectious processes like this suffer from a computational explosion – within a few days, millions of people are affected. (Due to the limitations of Twitter’s rate limits, I can only monitor a few hundred people an hour, so the disease is going to be self-limiting.)

This work touches on two related ideas:

Firstly, it looks at how we respond to incurable diseases like Ebola.

In real terms, the experiment will infect a few thousand people – a drop in the ocean to 645,750,000 registered Twitter accounts. Indeed, this reflects the risk of contracting Ebola for those of us outside the currently infected areas. I’m sitting in Brighton, my chances of being exposed to Ebola at this point it time is effectively nil.

But our response to outbreaks like Ebola reflect who we are, as a collective humanity. It makes us question how far our empathy extends, and how we share our skills and resources in a time of crisis. The only sane response is treatment and containment at source.

However, human nature skews us towards conflating the risk of infection with the horror of actual disease. Because the disease is gruesome, horrific and arbitrary, we have a different kind of emotional response than we have towards real, but intangible threats, like global warming.


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Secondly, it questions our apathy towards surveillance.

Algobola works across the network. The pattern of infection reflects social behaviours – it exposes who communicates with whom. This method of infection shares similarities with modern surveillance techniques. The number of ‘hops’ between you and a ‘person of interest’ can determine whether you are subject to further investigation, and can possibly result in real limits to your freedom.

Algobola explicitly exposes these kinds of connections, it shows how one random connection in your network may result in you being marked for ‘special attention’. Within a couple of hops the virus reaches thousands of people I’ve never met – when your government is ‘analysing your metadata’ the algorithms are working very much like a virus. Viruses are amoral, algorithms are much the same.

Will the introduction of this virus have any effect on Twitter behaviour? I’m not sure, I’m taking a baseline reading of how many mentions-per-day the user makes before and after the infection, so check back here for the results.

Update

Here’s what happened

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