I threw around a lot of ideas for this assignment. I wanted to create a generative art piece that was static and large–something that could be printed on canvas and placed on a wall. I also wanted to revisit the SMS dataset I used in my first assignment, because I felt I hadn’t sufficiently explored it. I eventually settled on modeling something after this “Triangles” piece on OpenProcessing. It seemed relatively simple and it was very abstract.

I combined the concept from the Triangles piece with code that scored characters in a conversation based on the likelihood that they would follow the previous characters. This was accomplished by generating a Markov chain and a character frequency table using combinations of two characters pulled from the full text of 2,500 text messages. The triangles generated to represent the conversation were colorized so that more likely characters were shown inside brighter triangles.

Process:

I started by printing out part of an SMS conversation, with each character drawn within a triangle. The triangles were colorize based on whether the message was sent or received, and the individual letter brightnesses were modulated based on the likelihood that the characters would be adjacent to each other in a typical text message.

In the next few revisions, I decided to move away from simple triangles and make each word in the conversation a single unit. I also added some code that seeds the colors used in the visualization based on the properties of the conversation such as it’s length.

Final output – click to enlarge!