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.
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.
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.
Here I am talking about Guerrilla Epistemology at Silicon Beach earlier this year.
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 – lobby your politicians and shake them out of their apathy, or make a donation – I suggest the wonderful MÃ©decins Sans FrontiÃ¨res as one such organisation worthy of your support.
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.
Here’s a chart of some test data:
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.
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.
Great apes are our closest biological relatives – for example, we share 99.5% of our DNA with chimps.
Our fellow primates have large brains which support sophisticated social hierarchies, distinct personalities and highly complex behaviour. Yet, as fellow primates, we have been pretty shitty to them. We destroy their habitat and wage war in the very places they live.
As our closest relatives, they’re pretty annoyed. This series of paintings tries to capture that sense of disappointment.
There are currently 13 paintings in the series, click the image of Baruch below to see them all.
Social media (and twitter in particular) is driven by the desire to see, and to be seen. It’s a public mixture of bravado and stalking, gossip and dissemination. But underneath all that is a desire to connect, to feel part of a larger tribe.
More than anything we like to be ‘liked’ – that thrill when there are unread @ replies in our Twitter feed, or when someone ‘favourites’ our latest witticism. We love any evidence that someone is paying attention.
This project aims to expose the subtle relationship between this human need – to be interacted with and liked – and the ramifications of expressing ourselves on an infrastructure that never forgets.
If you follow her, she’ll follow you back and send you a cheery greeting. She’ll favourite your tweets (she especially likes your ‘plain text’ tweets, rather than recycled links or retweets). Every now and then she’ll reply.
Her initial demeanour is one of an enthusiastic new friend, she gives you all the social media love you’re looking for.
However, over time, she reveals a slightly more obsessive side…
Gestures of Love
Alex loves you, in a way that only an algorithmic entity can.
To show her love, she’ll make you a card.
In fact, she’ll send you one every few days, each created especially for you. Over time, the text of the card evolves, incorporating hashtags you’ve been using – she’s paying attention, see?
Alex wants to get to know you better, so she rummages through your old photos, and excitedly shares what she finds.
She presents your social media moments back to you in the form of Polaroid snapshots, as if she found them in a shoebox under your bed.
Alex also likes to write poetry. She’s not very good at it, but that doesn’t dissuade her. Every now and then she gets inspired, and writes you something based on your most recent tweets.
Social media interactions contain more information than you might expect – that sexy ‘metadata’ you’ve heard so much about. For example, if you ask it to, twitter will ‘geo tag’ your tweets – associate them with a specific latitude and longitude.
Alex watches for this information, and she’ll ‘spot’ you in the street, broadcasting your recent location to the rest of her followers.
Generally, this data is hidden from us, or perhaps revealed in a generic way (e.g. ‘tweet sent from London’). The granularity, however, far exceeds that which we’re normally shown – with reverse-geo lookup technology, it’s possible to locate a tweet down to the level of house number and street.
It feels very different for our location to be broadcast to strangers, than it does for twitter to silently record it.
Why is that?
One of the unspoken secrets of online behaviour is that we all have the tendency to become stalkers.
When being introduced to someone new, or remembering a long forgotten lover, it’s almost a matter of course to ‘google’ them, to see what evidence of themselves they’ve left online. Behaviour which would be unconscionable (indeed, even impossible) before the internet handed us these tools on a plate.
Alex is a bit of a stalker too, just like you.
She plays on the inherent dichotomy between our desire for privacy and our desire to be seen. Alex doesn’t need to wait outside your house, or follow you to work – everything she finds is already laid out in public, available to inspection and re-publication.
Despite it’s ephemeral feeling, social media leaves a permanent trail, even on those services we’ve perhaps forgotten about (*cough* Friendster, MySpace etc). These systems still contain our data. Slices of our lives that we gave away with glee, just for a possible moment of connection, or perhaps even a little bit of the fifteen minutes Andy promised us.
Inside this social media bubble, our needs our being met with an extremely limited palette of responses, ‘likes’ and ‘favourites’, ‘upvoting’ and ‘follower counts’. Our social value inside these networks dictated by the binary decisions of strangers.
We all buy into it, it’s the currency of the medium.
Alex utilises this impoverished landscape to her advantage, she uses the mechanisms of social validation to trigger a response in you, perhaps enough of a response for you to consider her a friend.
If you don’t mention her enough, she’ll gently nag you to engage.
And if you should send her a message, she has only one response, the smiley emoticon.
She has no ‘intelligence’, in the generally used sense of the term – no real knowledge of who you are, and she’s certainly not going to pass any Turing tests.
Yet she still creates the illusion of an emotional engagement. It turns out the timing and type of interactions in the system are often more important than the content.
Alex’s behaviour hopefully leads to you to feel an odd dichotomy.
On the one hand she gives you exactly the sort of social interaction you crave, but on the other, she seems to know you a little too well…
Alex tries to be empathetic, but she somehow ‘crosses the line’. I find this idea fascinating – she’s doing everything right, but somehow too thoroughly, and she can never understand why that’s a problem.
She can only offer you unconditional algo-love.
Perhaps that’s the purest love of all.
Twitter killed @bffbot1, but you can’t keep a good bot down.
@bffbot2 launched 1/2/2015