With my limited timeframe, I was only able to examine so many posts, so many comments. But as I worked on this project and took a refreshed look at Douglas Coupland’s latest Slogans project Slogans for the Class of 2030 (2021), I began to wonder: Who is a Douglas Coupland fan? What would a hive-mind fan look like? Would it be interesting to gather all of the online responses to Coupland’s work, feed them into a machine, and “teach” it to speak? What kind of person would that be? Optimistic? Pessimistic? Happy? Sad? Would a project such as that help show what kind of person is drawn to Coupland’s work or show how Coupland’s work affects the psyche of his fans? Would a project such as this even be ethical? What impact would such a project have on Coupland himself?
I consider this not so much a project proposal per se but a thought experiment. With the advent of machine learning and publicly available data everywhere, we have the opportunity to perform a number of fascinating but extraordinarily morally gray experiments… I do have to wonder if Douglas Coupland himself would be interested in an undertaking such as this. His latest short story collection, Binge (2021) catches what he calls “the voice of the people” and was inspired by the way we write about ourselves and our experiences on social media. Indeed, each of the 60 pieces of flash fiction often sound like long, dramatic, viral posts… so I wonder… what is the unified “voice of the people,” voice of the Internet?
I wonder what the alternate-me would say?Instagram Commenter 2021
In terms of the realm of possibility, I am still quite interested in actually figuring out how to run a proper, rigorous, and scientific Topic Model / Sentiment Analysis on Douglas Coupland’s commenters’ textual output. In order to do this properly, it will take more research and time… it will also more than likely have to include a much larger source of data, one that goes beyond just looking at Slogans posts.