Prior to publishing our first data poems, it seemed only fair to let the data have its say. In this post, we’ll look at some poetry that’s been generated by data.

A quick Google search for algorithmic poetry returns a strong crop of projects that have generated poetry from data using algorithms. However, quality is the key, so here are three of the better algorithms that generate poetry.

New York Times Haiku

The New York Times Haiku Generator is an absolute gem. It uses an algorithm to search within The NYT’s website and pull out poems that conform to the broad rules of haiku poetry, namely the five-seven-five syllable structure for which haikus are most famous.

A few factors combine to make this a successful project. The scale of copy generated by NYT is an obvious bonus — the more copy you produce, the likely it is that suitable haikus can be sourced from your content. The US journalistic style’s tendency toward punctuation heavy, slightly traditional sentence structures and the general use of simple, short, direct words also helps.

Finally, the superficially simple five-seven-five structure is relatively simple from a programming point of view; the starting point is essentially searching through data for a single pattern.

Swift-speare

This was an experiment by J Nathan Matias, a genius at MIT, who created an algorithm that learns through experience. Matias fed his algorithm poetry by Shakespeare, Milton and others. He then asked it to produce a sonnet and achieved some pretty impressive results.

This project is an excellent example of the potential of machine learning and the work it produced was quite complex. However, in working to produce a Shakespearean sonnet, the parameters guiding what needed to be produced were pretty specific.

Poetweet

A much more interactive project, if not as pleasing in output, is Poetweet, launched by cultural centre b_arco, based in Sao Paolo, Brazil. Poetweet takes a Twitter account’s entire tweet archive and turns it into poetry.

The project is incredibly ambitious, allowing users to choose to create a sonnet or two rarer forms, a rondel or indriso. It works for tweets in both English and Portuguese.

The results are mixed but a great achievement when you factor in the complexity involved and that fact that users can generate poetry from any twitter account.

These three projects show the amazing possibilities provided by data in terms of creating poetry. They also show the limitations and the derivative nature of what’s currently possible. The next step in our journey is to create poetry from data.

Learn more about the Poetry by Numbers project here.

Can you have brown skin and a beard?

Beards mean different things to different people, but even in the cultural melting pot of London, beards on brown skin have a very specific meaning.

For most of my life, fashion trends have passed me by. My look has never really been of-the-moment and that’s never bothered me. When beards became fashionable, all of a sudden a trend appeared that I fully bought into. I grew a mighty beard.

I loved my beard and treated it well; I had it regularly trimmed and shaped and moisturised it every day (yes, really). I was really into it. Others, sadly, were not.

First came the jokes from friends and acquaintances, “Don’t expect to get on a plane any time soon.” Or, “You look like the Taleban.” And even, “You look like a terrorist.” These comments were a little hurtful but, ultimately, I chalked them down to ‘banter’ from friends. To be fair, when I told people the jokes were a little too much, they apologised and stopped. I’m lucky, I have brilliant friends.

Then came more subtle reactions. On public transport people stared at me much more. As anyone knows, staring at anything other than your phone, the Metro or into the distance is pretty unusual on the Tube. Worse still, almost all the time, the seat next to me became the last to be taken. In shops, assistants who were chatty to the customers before and after me weren’t chatty with me. At parties and drinks, people I was introduced to were less friendly at first. Less friendly until they saw me drink or had a chat with me.

All of this adds up to very little. However, anyone who has grown up in a place where they’re in the minority will know that most prejudice isn’t overt. It’s simple things and low-level barriers. Barriers that are easy to overcome, but the onus is on you, the other, to overcome them.

Growing up in London, and being part of a minority ethnic group, I’ve spent my whole life overcoming these minor things. Proving to people that I’m ‘alright’, that I’m ‘one of them’. Growing a beard made the barriers a little higher, the isolation a little greater, the hurt a little more bruising.

So I had it shaved off.

Now, I’m lucky, I didn’t grow my beard for religious reasons. It was vanity. Shaving it off wasn’t a big deal in the grand scheme of things. And now I’m back where I was. The barriers are a little lower again, but they’re still there.

I’m not a Muslim, but I’ve had a small insight into the challenges Muslims face. We ask them to integrate and be part of British society but we eye them with suspicion until they prove it explicitly. We do it to all minorities but for Muslims we’ve moved the bar up. It’s hard having brown skin and a beard in the UK, but the real problem is this: it’s becoming harder to be Muslim.

We’re constantly reminded of the huge volumes of data being collected by everything from our phones to the cars we drive. What’s often lacking, however, is any understanding of what the data means.

Without any irony, we’re inundated with statistics about the amount of data being created. As with much of the data, these statistics have no practical application.

Proponents seek to impress with size and hope the lack of substance will go unnoticed. The missing piece of the puzzle is context and understanding.

There are many people and companies who can help make sense of data sets through various means. There’s data science, data visualisation, infographics, dashboards and charts produced by insight teams, planners, data journalists, designers and research consultants.

The uniting element behind all of these activities and people is that they seek to unlock the complexity of the data they study and help others understand its meaning.

Data poets

Throughout history and across disparate cultures, one group of people have excelled at taking the complexity of the world around them, distilling it and communicating its essence in such a way that people make an emotional connection with it.

They move people. They do this through a variety of linguistic devices. They deploy rhymes and metaphors, they work with cadence, they play within stanzas and sonnets. They are poets.

The Charge of the Light Brigade by Lord Alfred Tennyson isn’t the only poem or record about the terrible waste of life in a war.

Yet it is the only one I remember with such emotion. I do so because the futility of the action and the bravery of those who took part was captured by Tennyson in a way that is deeply moving.

Were the Charge to be captured by today’s data specialists we would know much more about it, but we would not necessary feel the emotional connection to it that we do. It would be an exercise in analysis, not understanding.

World War I is known for its poetry. Despite the loss of life on an industrial scale, culturally we have an emotional connection to it. World War II, however, has an altogether different aura.

In both wars the sacrifice of those who perished and the scars of those who survived are great, but World War II is a media war, its popular memory captured by cinema.

I draw upon these battlefield examples because such loss of life is difficult to comprehend; the raw numbers of deaths and injuries do not bring to bear the full horror or valour of those involved.

Poetry by Numbers

It is for this reason that I’m launching this project to explore data poetry or, as I call it, poetry by numbers.

The project seeks to distil meaning from data by partnering poets and brands. Throughout 2015 it will commission poets to explore data. Follow our progress on this site where we’ll be publishing original poetry and commentary about how poetry and data combine.

A version of this post appeared here as a guest post on Stephen Waddington’s blog.

Learn more about Poetry by Numbers here.