The Mood of the Nation

As we approach the election, it becomes apparent that the narrative landscape of politics has changed. In the past, the discourse was led by inky newspapers and the satirical sniping of broadcaster-sanctioned comedians. The last election, in 2010, certainly had a social media component – manifested by the American-aping ‘televised debates’ and the associated back-channel chatter on Twitter. However, in the intervening five years, the size of this channel has increased enormously (approx 30m users in Q1 2010, to 288m users in Q4 2014)

It’s now possible to use this ‘big data’ to get a handle on the mood of the nation. Tweets can be read by algorithm and classified as positive or negative. Sentiment analysis is big business, harvesting the millions of opinions expressed online, and turning them into numerical values.

Brandwatch recently released sentiment data for David Cameron and Ed Miliband during the BBC Paxman interviews. In the video below I have used these data to plot the mood towards the two leaders during the broadcast – represented by red and blue backed emoticons. If the mood towards to politician is positive, the emoticon smiles, if the mood is negative, the emoticon cries.

Thousands of tweets, reduced first to numbers, and then to emoticons. Watching the result I’m struck by how the mood seems mainly negative towards both men. Is this a reflection of a national disenfranchisement with politics, or is it simply a reflection of social media itself – a place we go to complain, rather than praise?

@nk_markov

North Korea recently published a list of 300 patriotic slogans, which read remarkably like Markov generated sentences. I’ve never been a huge fan of Markov chains for creating text, since the results can be somewhat haphazard and ungrammatical. However, when the source text is itself haphazard and ungrammatical, it seems fitting.

@nk_markov takes these 300 aphroisms as source for a Markov system, and generates new pithy slogans and marries them to images of the glorious leader himself.




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Emojfication

The emoticon has become ubiquitous in informal text-based communications. The necessity to modify the meaning of potentially ambiguous sentences with the use of pictographic shorthand has a long history, but really came into it’s own with the democratisation of publishing which came with the advent of the internet. They are a visual shorthand which saves time in our busy lives. More recently we’ve seen the rise of the Emoji, which offers a more varied palette of face based symbols, conveying a range of emotions far greater than the manipulation of the ascii character can offer.


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Emoji Portraits by Jung Jake

These symbols almost universally take the form of a circular yellow face. This is a gender and race neutral form which finds it’s roots in the counter-culture of the 60s, and more recently in the Acid House scene of the late 80s. It’s interesting to see the symbol returning in the realm of fine art, notably in the works of Ryca and Jimmy Cauty.

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Alan Moore with a Jimmy Cauty riot shield

The human visual system has a lot of circuitry dedicated to the recognition of faces. Recognising the emotional states of our fellow primates clearly provides a strong evolutionary advantage. Indeed, it’s argued that the white sclera of the human eye developed to accentuate the expressiveness of our faces and make it easier for others to track our gaze. It is no surprise then that the face is used as a shorthand for emotion in written communication, it’s where we wear our emotions, and we’re built to quickly recognise them, even in the form of a yellow circle.


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Because faces are so important to us, we have trained computer algorithms to detect them automatically. I’ve used this technique in a number of recent works, notably Who Watches the Watchers? and @farageblocked. These algorithms are derived statistically from millions of images of faces – completely unlike our own system which seems at least in part innate, and certainly forms part of a more general visual system which works in an entirely different manner. This is most obvious when we find images mis-identified as faces, we’re suddenly drawn to see if we can see what the machine has seen, as in Henry Cooke’s Faces in the Cloud project.

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I recently modified @farageblocked to replace his face with miserable emoji and was immediately struck by how pleasing it was. I wondered whether the same technique could be applied to video, potentially providing a visual shorthand for remapping emotional states. There is a delightful tension between the obfuscation of identity and the immediate recognition of the emoticon.


2010 leader’s debate

As our social interactions move away from the realm of reality and into the online world, we are seeing an increase in social anxiety – we seem more comfortable communicating our emotions via the similey face than dealing with the reality of our fellow humans. Apps like pplkpr promise to simplify our emotional landscape by monitoring our stress levels and advising us who to avoid.



Waiting for Godot (excerpt)

Could we imagine a future where this anxiety is eased by a form of ’emoticon subtitling’? Where our already mediated interactions are automatically overlaid with a simplified palette?


Shake it off – Taylor Swift

The technique lends itself to directly inverting the intended meaning of the source, for example:


Happy (sad) – Pharrell Williams


Stagger Lee (happy) – Nick Cave and the Bad Seeds

2014 review of the year

Trite as it may be, the end of the year offers an opportunity to review our deeds, and plot future (mis)adventures. Herewith, a review of my year of aesthetic experimentation, 2014.

The year started with messing with the media of political discourse. Both David Cameron, our Prime Minister, and Ed Miliband, the leader of the opposition, released ‘New Year’ messages. The blandification of British politics was laid bare by the similarities between the men and the vacuous messages. I’ve algorithmically blended politicians before, but this time I (mis)used the marvellous Echonest API to literally put their words into each other’s mouths.




I crashed my bike in February, getting knocked out, breaking some bones and being saddled with Trochlear Nerve Palsy. I subsequently spent 5 months with an eyepatch, inciting pirate jokes wherever I went. Not much art was produced for a while as a result, and I had to learn to paint with one eye. I did manage to speak at #pydata, whilst still somewhat concussed.

Inspired by the lies and clickbait which seem to make up much of the internet, I released a lying twitterbot. @factbot1 makes up facts, finds a suitable image, and posts them online every 4 hours. The account is still running, and as I write this has just produced it’s 1,500th lie.

Then there was @hipsterbait1 – an experiment in algo-commerce. Could a bot produce a work, and offer it for sale through a third party, automatically, without any human intervention? The bot produces t-shirts that mash up images and references, primarily in the domain of band t-shirts. Unfortunately, my plans to retire on my algo-generated fortune were nixed when Zazzle quickly refused to actually print them.

bffbot1

June brought one of the more sophisticated bots of the year, @bffbot1, an algorithmic stalker who aimed to be your best friend, writing you poems and spotting you in the street. She was very popular, particularly with the Turkish (not sure why) until she was killed by Twitter in October.


September was filled with curating and producing The New Sublime at Brighton Digital Festival – a fantastic group show of some of the finest artists working with digital technology.

It was a busy month where I finished a series of 13 paintings called ‘pissed off primates‘, and embarked on a brief international speaking tour which took me to Canada, London and Bournemouth.


At the end of October I produced another bot which also fell foul of Twitter – a simulation of social infection called @algobola

I also knocked up a bot with all the answers, painted Rik Mayall and Chris TT. I got some robots to perform Waiting for Godot, built systems to scrape folk fan art from twitter, compress great works of fiction, and most recently excise the face of Nigel Farage.



So that’s my 2014 in a nutshell, expect more of the same in 2015 – follow me on Twitter and be amongst the first to know…