While I have had a WordPress blog for a while, I have only very recently enhanced and updated it. I was able to do much of this work for a wonderful course I took last year (Spring 2021) called CCTP-850 Digital Presence & Strategic Persuasion. This course was aimed at students who sought to transform their digital presence into one that resembled that of a working professional. I believe I succeeded in meeting that course’s goals, but there were, of course, some difficulties:

• During the course, I attempted to combine both a working professional’s website with that of an academic’s… I think I succeeded in this? It’s a little tough to say because some elements of those two cultures simply do not mix well.
• My WordPress blog has some limitations… one of which was I couldn’t really create a nice looking timeline. Our instructor showed us numerous clean, professional looking timelines during the course that she recommended as tools for creating interesting looking résumés on our webpages. My WordPress blog’s inherent restrictions precluded me from utilizing them. Sad!

I really wanted to make a timeline. But my blog, being a WordPress dot com blog, is not compatible with plugins unless I spend a large amount of money. I’ve continued to struggle with WordPress in other areas as well… one thing I had hoped to do (and admittedly, this may be a problem with Instagram, another application that has given me grief over the years) is embed posts directly into webpages, when I try, this happens:

View this post on Instagram

A post shared by Doug (@douglascoupland)

You can’t see anything! You have to actually follow the gigantic, space-hogging link to Instagram and view the post on the account owner’s page. WordPress includes one block—which I, incredibly, actually have access to—that will allow you to actually view posts on your page… but it only works with your account, and it appears to only show most recent posts, not a personalized selection.

During this semester’s (Fall 2021) ENGL-726-01 course, Digital Approaches to Literature… I was determined to get a win. When the course professor showed us Knight Lab’s Timeline JS I knew that I had to incorporate it into my final project. So I worked to do so, and created a timeline that you can view by following this link.

Yeah, you have to follow the link. Once again, I was sad to discover that my WordPress blog was not compatible with a timeline… according to Knight Lab, in order to make the timeline work on your blog’s page you need—you guessed it—a plugin. So I’ve done some research and have actually discovered that there is a way around this issue that, at least, at a first glance, appears to be relatively cheap. You can actually convert your WordPress dot com blog into a WordPress dot org blog… and, a WordPress dot org blog will actually allow you to use plugins without having to spend a whole lot of money. The process is a little complex, but I found numerous blogs—such as this one—that can assist with one such as myself who might like to do this one day.

Despite being unable to embed my Timeline JS directly into my WordPress blog… I’m keeping it. I’ve wanted to make a timeline for so long that I am already way too attached to it to even consider giving it up. And how could I not be? Knight Lab’s Timeline JS tool is incredibly easy to use and produces such a high-quality visualization.

I found that Knight Lab’s 4 step guide to making a Timeline JS was comprehensive and easy to follow… I did not even need to watch the brief tutorial video they made. The only issue I ran into was that I had not realized that its software could not read text edited using Google Spreadsheet’s text editor. This proved to be a simple problem to overcome, however, and I was able to use some of the HTML language we learned earlier in the course to add links and some color to my timeline. When searching for pleasing colors, I found a website called HTML Color Codes that proved to be invaluable help, it made it really easy to find appropriate HTML color codes for any color that I was seeking.

So while I do not have a high-quality timeline embedded directly in one of my project’s pages, you will still be able to find one linked in my project’s Table of Contents… My hope is that the linking back and forth between the Timeline JS and my final project is intuitive and unobtrusive.

In a previous blog post, I detailed the most prevalent difficulties I came across when working to create my corpus. These difficulties, culminating with the act of having to manually collect around 1,400 comments, resulted in me taking a number of unintentional shortcuts that hindered my data analysis. Luckily, during the Q&A period of my project presentation, a number of my fellow students were able to bring to light a couple of problems resulting from these shortcuts which I have since begun to try and rectify.

For my data analysis, I chose to use Voyant Tools both because I found it easy to use and because it ran pretty well on my six-years-old Mac Mini (its on its last legs, unfortunately). I like Voyant Tools because it has a large number of help documents that provide insight into each and every individual tool on the platform. The platform’s interface itself is also very clean and easy to use and understand. I had no problem creating new stop words, adjusting the parameters for my preferred tools’ analysis, and swapping out tools on a whim. The platform occasionally crashed… but honestly that could just be an issue with my computer. 

When I made my first attempts at text analysis, I used a single corpus made up of about 1,400 essentially unorganized comments taken from Douglas Coupland’s Instagram account. My hope was that this corpus would grant me a bit of insight into who Coupland’s commenters were; or, what kind of person is drawn to Coupland’s work.

Using this technique, I was able to pick up some, I would say, minor details. Using Cirrus, I was able to see a high-level view of the most used words in the comments. I found an interesting amount of positive language; love was used far more often than hate, like more often than dislike, yes more often than no, true more often than false, good more often than bad. Also, based on this wordcloud, people are clearly trying to get Coupland’s attention… his @ and his familiar name “Doug” appear with a surprising amount of frequency… the marker of an account that sees a lot of interaction from real world friends… or of an an account that has a large amount of folk stuck in a parasocial relationship.

There are, of course, a handful of issues one that I picked up on (and that was emphasized by my peers during the Q&A period of my presentation) is that folk tend to not communicate in a standardized way when writing online. I discussed this problem in my blog post on Topic Modeling; but, essentially the problem is in a handful of words: People say “not good” a lot and that’s… well, it’s not good.

My hope was that, using topic modeling (available through Voyant Tool’s Topics tool), I’d be able to perform a rudimentary kind of layman’s Sentiment Analysis (also discussed in the aforementioned blog). Using my original corpus, this proved to be… sort of doable, but with extremely mixed results. Running this procedure a couple of times, I noticed a couple of interesting topics: COVID anxiety / fear (which was in turn a source of many “negative” comments) and love for Coupland and the desire to see more of his work and, in particular, a new book. But there were a multitude of other topics that, at best, appeared to be random.

During the Q&A period, another peer of mine recommended that I look at a handful of Slogans with a high-volume of comments, create unique files of comments for those Slogans, and then use those individual files for my text analysis… I discussed some of the reasons why I originally did not take this approach in a previous blog post, but ultimately, my peer was right, and the benefits of this approach outweighed the added annoyances that came with collecting the data. The resulting materials from Voyant Tools proved to be much more readable when I fed it data in this format, uploading TXT files collecting comments from six Slogans: 

• WILL YOU WEAR A MASK TO PROTECT ANTIVAXXERS?
• I AM NOT ALLOWED TO VISIT MY MOTHER ANYMORE AND IT IS BREAKING MY HEART.
• MONEY’S ALMOST OVER. WHAT REPLACES IT?
• WORLD WAR THREE IS NEXT
• WHAT IF IT’S FINALLY ENDING?
• I LOVE THE PEOPLE IN MY LIFE

The resulting output included much more usable results; or at least, results which I could use to tell an interesting story.

It’s this material that I have discussed in my final project, found: here.

Perhaps the most difficult step in my project was figuring out just how to collect a large bundle of comments from Instagram. Through my research, I learned that Social Media researchers preferred method of data collection involved using AI Assisted “Scrapers; “Data Scraping” is essentially a computer program that extracts data from a website and outputs it as human-readable data that ideally will be compatible with other data analysis program… I then learned that, in recent years, Instagram / Facebook (now Meta) has done absolutely everything in its power to essentially resist these tools’ usability. That’s not to say that techniques do not currently exist, but what I ultimately found was that techniques would either:

• Require a rudimentary understanding of Python that I both lacked and was unsure I could become proficient in given the course’s limited timeframe.
• Are fairly costly or had extremely limited free trials that would limit what I could do and which could have become problematic if I didn’t have a rock solid research plan.

I attempted to use a free option, namely Webscraper.io… but despite being relatively straightforward, I had great difficulties actually getting it to work on Instagram… I do think that this tool could be used to scrape Instagram comments… but I am just 100% unsure how to get it to do so.

During the course of researching both why Scraping Instagram had become such a difficult process and how one could work around those difficulties and still do it, I stumbled onto this interesting blogpost called “Instagram Scraping in 2021” by Vlad Mishkin (2021) that… to be quite honest, I have some difficulty fully understanding. But, with that said, I think that with time I actually might be able to follow his methods and perhaps get this particular code to work… I may still try the method he outlined in the future, but for now I am constrained by time. 

This left me with one extremely unfortunate option: I had to go in and copy / paste as many comments as I possibly could into an Excel file that could then be converted into a TXT file ready for data analysis. This process unfortunately took weeks… many late nights (that were at least soundtracked by some decent music). Given Instagram’s (seeming?) instability on web browsers, I had difficulties extracting comments using this technique as well. The first problem is that a posts’ comments must be “loaded” multiple times by pressing a button in order to gain access to the entire comment thread. This is not exactly as straightforward as it seems on the surface… sometimes, you press the button, and it simply re-loads the comments you have already loaded; other times you will press the button, and it will collapse the entire thread to its standard state. I often found that I would have to load / unload comments and close out / reopen posts multiple times in order to get the full thread in view.

To get around the finicky nature involved with manually Scraping an entire post’s comment thread, I decided to collect the top comments from each post. This resulted in about 1,400 individual comments from around 140 posts. During my project presentation, some of my fellow classmates brought up a couple of issues worth considering:

• How are the comments organized? 
  • This is tricky… as it seems kind of unclear. Based on my experimenting, it appears that they are organized chronologically with the more recent comments appearing at the bottom of the thread. But other research I’ve done states that comments from verified accounts or high-follower volume accounts will be prioritized over smaller accounts. This would mean that I would have collected comments from first responders, verified responders, and responders who have a lot of followers.
• What did you do about emojis?
  • I did not collect emojis. This could be a problem because in some ways; emojis set tone. But with that said, most comments that included emojis solely included emojis… most comments with text solely included text. I do not think this decision will have an outsized impact on my study.
• Why not look at multiple posts with a high-volume of comments?
  • In a phrase: Because that would have been annoying… But… yeah, I should do this. So I did. I collected all the comments from 10 posts with a high-volume of comments resulting in about 500 additional comments.

So now, I have a corpus of about 1,900 comments with which to perform a multitude of experiments on. I have detailed that process here.

Douglas Coupland’s fiction is somewhat difficult to classify, he is certainly more of a “literary” writer, but he has, occasionally, delved into the realm of genre, dealing with the post-apocalypse in Girlfriend in a Coma (1998) and Player One (2010) as well as the near-future in speculative novels like Generation A (2009). Other novels, like Microserfs (1995) and jPod (2006) have Sci-Fi-like flourishes and a difficult, almost paranoid relationship with technology. His textual art is equally difficult to class. Take Slogans, the concept of the project itself is at least somewhat clear—in Coupland’s (2020) words, “What could I tell myself 10 years ago that would make no sense to that old ‘me’?”—a flipping of Jenny Holzer’s Truisms (1978-1987) which she described as “[her] Reader’s Digest version of Western and Eastern thought.” But in terms of their genre… it’s tough to classify. Most are likely simple maxims… but others appear to delve into the realm of memoir… some look like news article headlines… some are simply absurd… and a handful are eerily prophetic.

In a piece for Image Journal called “Search Results: The Real-Life Douglas Coupland,” Mary McCampbell, a professor at Lee University and a scholar of Douglas Coupland’s work wrote that “the more [she] read Coupland’s work… the more [she] saw his writing as prophetically exposing the sins and longings of contemporary capitalist North America.” Typically, perhaps with the exception of religious writing, prophecy would not be considered a “genre” per se; but, there is something interesting about looking at his work through that lens… he did, after all, write a book called Hey Nostradamus! (2004). With regards to his Slogans, Coupland (2020) himself noticed a fascinating development upon the advent of the COVID-19 Pandemic in 2020, he said in his article for The Guardian, “Signs of the Times”:

“Overnight the inventory of once-esoteric slogans somehow magically came to life in a way at once menacing, unexpected, deadpan, activating and prescient. Slogans such as hoard anything you can’t download, or, healthy people are bad for capitalism or the present and the future are now the same thing unexpectedly chimed with our experience of the plague: the 1%, the left, the right, the middle class, the disenfranchised, the globe, the young, the old, the you and the me.”

Douglas Coupland (2020)

It’s not exactly prophecy… but it’s certainly… weird, one might say uncanny, that Slogans created up to a decade ago would only become more relevant to the artist’s culture overtime. In the year since, Douglas Coupland embarked on a new project, Slogans for the Class of 2030, a project that actually might be a genuine attempt at prophecy, albeit with a more Sci-Fi rather than religious flourish. In order to create this new batch of Slogans, Coupland worked with Google’s Nick Frosst, and together they fed Coupland’s written words into a machine learning system that then recombined his language and created new sentences and phrases. The end result of this is the handful of new Slogans that make up Slogans for the Class of 2030, a work that Coupland hopes will serve as inspirations for young people who will graduate from university in 2030.

I keep thinking of the assigned blogpost that Ted Underwood, a professor of Information Sciences and English at the University of Illinois, Urbana-Champaign, wrote titled “Science Fiction hasn’t Prepared us to Imagine Machine Learning” in the context of Douglas Coupland’s newest Slogans project. His blogpost notes the unexpected shift that Machine Learning has taken in recent years, one that is a step away from the self-reasoning AI often depicted in Sci-Fi and a step towards “deep learning,” or, the extreme ability to find correlations between words. Underwood describes this as an unintelligent system with the ability to add to collective symbolic systems (like culture) … he describes this as a tool that could, perhaps, be more interesting than AI, stating:

“After all, people are already very good at having desires and making plans. We don’t especially need a system that will do those things for us. But we’re not great at imagining the latent space.”

Ted Underwood (2021)

Douglas Coupland (2021) described the process of reading outputs from Google’s machine learning system as feeling “like [he] was encountering a parallel universe Doug.” Essentially, he was doing exactly what Ted Underwood described in his blog, collaboratively exploring the latent space of possibility. Underwood mentions Jorge Borge’s sort story “Library of Babel” to sort of illustrate the concept of an author’s latent space… and thinking about Borge’s story, and Coupland’s collaboration with Google’s Machine Learning System I am wondering… why stop at Slogans? What would a truly collaborative novel work between a machine learning system and an author look like? Are there infinite numbers of works resting in author’s minds?

A part of me thinks that the answer to these questions is essentially “no.” After all, authors, and artists of all kinds often find a point in their careers when they feel they’ve “said all they can say.” In a sense, silence… or that “latent space” is a statement, too, and sometimes it hold more meaning than words ever could.

Works Cited:

Coupland, Douglas. 2020. “Signs of the Times: How Douglas Coupland’s Art Came to Life under Coronavirus.” The Guardian, May 29, 2020, sec. Books. https://www.theguardian.com/books/2020/may/29/i-miss-my-pre-internet-brain-slogans-coronavirus-douglas-coupland.

———. n.d. “Slogans for the Class of 2030 by Douglas Coupland.” Google Arts & Culture. Accessed December 10, 2021. https://artsandculture.google.com/story/slogans-for-the-class-of-2030-by-douglas-coupland/vQURjwPHHpa5uw.

Holzer, Jenny. n.d. “Jenny Holzer. Truisms. 1978–87 | MoMA.” The Museum of Modern Art. Accessed December 10, 2021. https://www.moma.org/collection/works/63755.

McCampbell, Mary. n.d. “Search Results: The Real-Life Douglas Coupland.” Image Journal. Accessed December 10, 2021. https://imagejournal.org/article/search-results-the/.

Underwood, Ted. 2021. “Science Fiction Hasn’t Prepared Us to Imagine Machine Learning.” The Stone and the Shell (blog). February 2, 2021. https://tedunderwood.com/2021/02/02/why-sf-hasnt-prepared-us-to-imagine-machine-learning/.

My final project heavily relies upon the study of the “comments section” attached to the Instagram postings on each of Douglas Coupland’s “retooled” Slogans (2020-Present). Why? I’ve made a small claim in previous posts / pages on this site stating that a Slogan (2020-Present) on Instagram must be viewed in conjunction with the metadata that comes attached to its posting, data which includes: comments, post caption, date of posting, location of posting, and number of likes. The Slogan (2020-Present) on Instagram is thus the whole post and, in my view, should be studied as a sort of secure communal square… as an illustration, consider a man standing on a busy street next to a large signed and dated billboard he created with a slogan plastered onto it; the man has a jar of markers on a stool next to the sign and folks begin to pick up markers and write notes onto the billboard. At some point, someone walks up and takes a photo of the billboard. For the purposes of this study, I would essentially be studying that resulting photograph.

I mentioned Sebastian Veg’s (2016) statement that: “the exchange of slogans can be viewed as a constitutive type of ‘communicative action’ (pg 691)” on my webpage. I am concerned with uncovering what the community of Douglas Coupland commenters is acting on. To do this, I have been exploring the methodologies of Topic Modeling and Sentiment Analysis.

Topic modeling is a tool that utilizes an algorithm to seek at the most frequently used meaningful words in a corpus and then organize them into select groupings or “topics” based on those words’ relationships to each other. Scholars Andrew Goldstone and Ted Underwood (2012) described topics as: 

“A “topic model” assigns every word in every document to one of a given number of topics. Every document is modeled as a mixture of topics in different proportions. A topic, in turn, is a distribution of words — a model of how likely given words are to co-occur in a document. The algorithm (called LDA) knows nothing “meta” about the articles (when they were published, say), and it knows nothing about the order of words in a given document.”

Andrew Goldstone & Ted Underwood (2012)

One of the more widely used algorithms for this purpose is Latent Dirichlet Allocation (LDA). According to Benjamin M. Schmidt (2012) in his article “Words Alone: Dismantling Topic Models in the Humanities,” this particular algorithm was first described by David Blei, a computer scientist who specializes in machine learning, in 2003. Blei and his group viewed LDA as an advance in information retrieval; Schmidt (2012) states that this is quite different from the algorithm’s use in the Digital Humanities where it is often treated as a tool for discovery. In this dichotomy, retrieval would tell a researcher what texts they might find interesting; discovery would tell a researcher something new about the text itself. 

I had a little bit of trouble understanding the difference between retrieval and discovery, but Benjamin Schmidt (2012) linked to a fun allegorical blog post titled “The LDA Buffet is Now Open; or, Latent Dirichlet Allocation for English Majors” by Matthew L. Jockers (2011) that clarified things for me… the article’s about authors at a buffet picking out topics to put on their plates. Another helpful article Schmidt (2012) linked to was Andrew Goldstone’s and Ted Underwood’s (2012) “What Can Topic Models of PMLA Teach Us About the History of Literary Scholarship“; the conclusion of their piece states that a topic model states both what people are writing about and how (how relating to “academically” or “laymen-ly” etc).

This how, however, does create a number of problems, some of which Schmidt (2012) addresses in his article… a significant one, and one I am concerned about in my own study is: “long term drifts in language,” including changes in language that not be “topically coherent.” While I do not have to worry about “long term drifts” I do have to worry about the odd nuances of Internet-speak, where phrases like “not good,” or words like “ok, okay, O.K., okie, ‘k, k, kay” are common. Could the common misspellings and odd phrasings common on the Internet derail my study?

While Googling around, I stumbled on this article by Nuo Wang, a chemistry PhD who wrote an article called “Topic Modeling and Sentiment Analysis to Pinpoint the Perfect Doctor.” Her article detailed a project where she used a tool to consolidate troves of reviews on doctors using LDA and sentiment analysis; she called the resulting tool “DoctorSnapshot.” This tool analyzed developed topics—later defined into 11 specific topics by Wang—like “bedside manner” and then detected either “positivity” or “negativity” in reviewers’ discussion of those topics. This project appears to be extremely well-developed, and I can see it being a useful guide for someone such as myself who is explicitly trying to define exactly who commenters on Douglas Coupland’s Instagram account are: What are their beliefs? What are their concerns? What do they like? Etc.

Nuo Wang’s methodology at the moment exceeds the scope of my abilities, however, this class helped bring her discussion into the realm of my understanding. I hope that, perhaps with some time and extra effort, I will be able to perform a similar-style analysis on commenter text. It could be a boon for a future project that I am working on.

Works Cited:

Goldstone, Andrew, and Ted Underwood. 2012. “» What Can Topic Models of PMLA Teach Us About the History of Literary Scholarship?” Journal of Digital Humanities 2 (1). http://journalofdigitalhumanities.org/2-1/what-can-topic-models-of-pmla-teach-us-by-ted-underwood-and-andrew-goldstone/.

Jockers, Matthew. 2011. “The LDA Buffet Is Now Open; or, Latent Dirichlet Allocation for English Majors | Matthew L. Jockers.” 2011. https://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/.

Schmidt, Benjamin. 2012. “» Words Alone: Dismantling Topic Models in the Humanities.” Journal of Digital Humanities 2 (1). http://journalofdigitalhumanities.org/2-1/words-alone-by-benjamin-m-schmidt/.

Veg, Sebastian. 2016. “Creating a Textual Public Space: Slogans and Texts from Hong Kong’s Umbrella Movement.” The Journal of Asian Studies 75 (3): 673–702.

Wang, Nuo. 2017. “Topic Modeling and Sentiment Analysis to Pinpoint the Perfect Doctor.” Insight. November 21, 2017. https://blog.insightdatascience.com/topic-modeling-and-sentiment-analysis-to-pinpoint-the-perfect-doctor-6a8fdd4a3904.

My project is a continuation of work that I began over a year ago during my second semester in Georgetown’s Communications, Culture, and Technology program. The first iteration of this project looked at Douglas Coupland’s Slogans as textual art and took the form of a rather over-long blog post titled “The Past Is A Bad Idea / The Future Is Fake / The Present Is…” Several months ago, I had the opportunity to expand on the research I did in that class and present a second iteration of this work, a new paper called “Guts Glory Copy Paste,” to the first Douglas Coupland conference, “The Art of the Extreme Present.” That paper looked at Slogans in the context of advertising and COVID-19. This new, as of now still untitled work, builds on both those previous iterations and attempts to look at Slogans as they exist on Instagram with the notion that, on Instagram, the entire post—not solely the image (or Slogan) itself—is the artwork to be studied.

I rewrote this work several times in an attempt to find a good frame for this argument… The first iteration framed Slogans within the context of textual art. The second iteration framed Slogans within the context of advertising. This iteration frames Slogans within the context of protests by using the Umbrella Uprising and the scholarship surrounding the produced media during that event as a guide.

So what are Slogans? Well, they’re this:

And who is Douglas Coupland? Well, he’s this:

Begin Vocal Presentation Here: Coupland is a writer and visual artist who wrote some novels that meant a lot to me while I was an adolescent in middle school and high school. In addition to being a man with a talent for weaving a good tale, he has a penchant for—for lack of better phrasing—doing things with words on the page. You’ve heard of writers who play around with language; this is a writer who plays around with words, that is, physical words.

A common page from a Douglas Coupland novel will look something like this:

And here are some Word Clouds:

In addition to being a writer who plays around with words, Douglas Coupland is a writer who recycles his words, recontextualizing them across time and space. During the course of my research I read a book chapter by Wing-Ki Lee (2018) called “Derivative Work and Hong Kong’s Umbrella Movement: Three Perspectives.” In that paper, he discussed the genre of derivative work, how it was produced during the Umbrella Uprising, and what could be derived from its proliferation. Lee described “derivative work” as: “digital image manipulation created by both known and unknown (anonymous creators) and their viral dissemination on the Internet (pg 31).” Lee was speaking specifically about manipulated photographs that had become manipulated memes on the Internet… but, I could not help but see parallels between the specific phenomenon he was discussing and Coupland’s 2020 practice of uploading old, “retoooled,” and new Slogans to his Instagram account.

This new exhibition of Slogans looks like this:

In a speech for Long Now, Douglas Coupland described the general concept behind Slogans to be: “What could I tell myself 10 years ago that would make no sense to that old ‘me’?” In a pre-charged, pre-COVID context, in the context of a comments free setting like a museum exhibition, that concept is wholly uncontroversial. But on Instagram… things are different. Instagram is, for better or worse, a public sphere. When Coupland posts a Slogan onto Instagram… on occasion… people take issue with it.

Take NEW ZEALAND SOMEHOW NEEDS TO BE PUNISHED:

“What? Why DC, why?!,” “Wha????,” “Unfollowing.”

Instagram Commenters (2020)

Coupland’s Slogans on Instagram are an interesting textual artifact because, while they are clearly art of some kind, they are often treated as public opinions given by the artist himself, or jokes / memes, or as literal Slogans meant to define… something, a community of some kind?

But what is the overall sentiment of this community? To answer this question I looked through each Slogan posting and collected up to 10 random comments from the post. I then ran those comments—about 1,500 of them—through Voyant Tools, an open-source, web-based application for performing text analysis.

Using Cirrus, I was able to see a high-level view of the most used words in the comments. I found an interesting amount of positive language; love was used far more often than hate, like more often than dislike, yes more often than no, true more often than false, good more often than bad. Also, based on this wordcloud, people are clearly trying to get Coupland’s attention… his @ and his familiar name “Doug” appear with a surprising amount of frequency… the marker of an account that sees a lot of interaction from real world friends… or of an an account that has a large amount of folk stuck in a parasocial relationship.

Using Topics, I was able to see the results of some rudimentary topic modeling. Running this procedure a couple of times, I noticed a couple of interesting topics: COVID anxiety / fear (which was in turn a source of many “negative” comments) and love for Coupland and the desire to see more of his work and, in particular, a new book.

Using Phrases, a tool that finds repeating phrases, I discovered a community of commenters that was tuned into the world of Coupland: I found lyrics to an R.E.M. song (one of Coupland’s favorite bands / his personal friends), 4 references to Jenny Holzer, his greatest influence, and 2 references to Girlfriend in a Coma, a post-apocalyptic novel that perhaps could be called “prophetic” when discussing this current era.

This past summer, Douglas Coupland worked with Google research scientists to develop new Slogans for the class of 2030. The process involved a partnership between Coupland and an AI that had “learned” to speak his language through machine learning.

This resulted in Slogans like this:

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, 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?

I don’t know if it is an avenue for future research per se… but it is an interesting thought experiment.

Imagine:

AI Art with AI Fans.

This is but one possible conclusion I could come to upon working to complete this study. It remains incredible to me that these short, punchy Slogans hold such complexity. I look forward to continuing my study of them for years to come.