@happyB0T, @sadB0T

happy not sad, sad not happy

     happyBot     sadBot

I’ve set up two twitter bots that follow identical algorithms but will probably evolve to be very different. Both @happyB0T and @sadB0T are learning Markov text generators. Their vocabulary starts from zero but they gradually expand it by searching for tweets and adding them to their own growing dictionary of words and phrases. They then use a Markov chain algorithm to construct new sentences and tweet them. 

The difference between the two is that happyB0T only searches for tweets that include :) (happy faces), and sadB0T sadly only collects tweets that include :( (sad faces). 

More specifically in code and twitter api search terms…

searchQuery = “:) -:(“; //happy, not sad

searchQuery = “:( -:)”; //sad, not happy

The bots also serve as fragmented mirrors to our cultural and linguistic tendencies. Only time can tell how these two will progress. 

A project done in Processing with Twitter4J and RITA libraries, by Matthew Plummer-Fernandez.

Note: the ‘0’ in happyB0T and sadB0T is actually a zero 0. (For twitter naming purposes)

Tangled in New Aesthetic

Warm gratitude to Aaron Geiger for publishing this interview on his journal butifandthat.com

 and a special thank you to James Bridle for publishing Sound / Chair on his popular tumblr 

and a brief note on @NArtBot for now: NArt Bot is a twitter bot that monitors the online discussion surrounding the New Aesthetic. It also participates in the discussion by reading the NA essays that have been published and generating new sentences out of it using a Markov algorithm. An update I’m about to implement today will help NArtBot learn from its interactions. From now onwards any Retweets of what it has said will ‘teach’ it to know that that choice of words had a positive outcome, and would feedback into the Markov algorithm. I don’t know what the outcome of this will be, we’ll have to wait and see!