Sorry I’m so drunk I can’t dance with you #OppositeDay @midnightExcerpt from Troll Talk
Yeah I’m too naked I would probably sing with you #Jodyhateshisdad @midnight
Eating Cheetos naked, I’m good. #CancelPlansIn5Words
Survivalguidetothanksgiving Cam drunk, I’m fun. #Imma
I’m not drunk, I’m flirting! #ReasonIAmDrunk
I’m probably naked, I’m flirting! #Kitten
I’m sad, I’m broken, I’m unhappy, I’m in tears, I’m let down, I’m disappointed, I’m hurt but I smile, that’s life.
I’m stupid, I’m drunk, I’m unhappy, I’m in tears, I’m let off, I’m annoying, I’m catch and I smile, that’s reality.
Earlier this summer FiveThirtyEight shared a corpus of nearly three million tweets associated with accounts linked to Russia’s Internet Research Agency. The evidence suggests these tweets were part of a campaign to influence the 2016 US election. What was communicated, and how do we make sense out of it?
One possibility is to simulate a conversation among the trolls using machine learning and data science techniques.
For the 2018 edition of NaNoGenMo I have written code that generates a conversation based on what the Russian trolls posted on Twitter. Occasionally the code produces interesting text: instead of serving to incite flame wars and dissension on Twitter, the corpus of tweets contains countless potential discussions that move in thematic directions. We have computational tools that allow for exploring algorithmically rhetorical invention.