Markov Chains, Twitter and Radical Texts

The next few posts will cover some pet projects that I did whilst not being able to work due to recent civic duty.  They cover things from the role of familiar strangers on the internet and anti-social networks, through to meteorological hacks, funny memes to twitter bots. The first in this series is about what happens when you use markov chains and radical texts with twitter.

Detournement is a technique now considered to the father of remixes or mashups, but with a satirical political nature. Have a look at the wikipedia entry for detournement if you want to know more about it. Basically you do something to something which twists or re routes it so that it makes new meanings. It was the Situationists, led by Debord who really adopted and ran with this as a practice.


Debord would often frequently plagiarise other radical texts in his own work. (The Situationists were also the ones behind original notion of psychogeography – something that you may have caught me talking about before.)

So what would happen if we could detourn, or mashup, or plagiarise Debord’s own writings? And how about if we could publish it periodically, and how about if we had a 140 character limit? Yeah so this is my experiments with these ideas.

Bruna Rizzi; it is from this disastrous exaggeration. The peasant class could not recognize the practical change of products

The proletariat is objectively reinforced by the progressive disappearance of the globe as the bureaucracy can

Markov chains basically work like take a couple of sentences: “A lazy dog likes cheese” and “My house likes to be clean” then look at groups of two or three words together. Then if one of these groups share the same word (“likes”), make a new sentence using that word to chain together. “My house likes cheese” or “A lazy dog likes to be clean”. Markov chains result in sentences that look human readable. The more sentences you feed the population sample, the better or more varied the same of generated sentences.

Some radical texts are complete nonsense and really hard to read, so perhaps applying Markov chains to them can help reveal what truths the obscure language hide.

@markov =
@markov.parse_file "debord.txt"
raw_text = @markov.generate_23_words

My solution uses Ruby, the Twitter gem and the marky_markov gem.  is the work in progress twitter bot – it works currently on Heroku using the scheduler to periodically tweet a sentence, see if any other users have asked it questions and reply back to them.

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