When you’re a college professor, you follow a different calendar from the rest of the grown-up world. There’s school and there’s summer, and that’s how you plot your time. Of course, a global pandemic wreaks havoc on this calendar. But usually, somewhere about now I stop thinking about the previous academic year and start looking ahead to the next one. My New Year begins on July 1, not January 1.
Since I’m closing the books on the 2019-2020 school year, I wanted to remind myself of all the projects I put out into the world during this time. Here in one place are all the critical-creative digital works I released in the past 12 months. I’ll write more about many of these projects later, so right now a blurb for each will have to suffice. Hopefully that’s enough to pique your interest…
Ring™ Log (October 2019) – imagines what a Ring “smart” doorbell cam might see on a Halloween night
An End of Tarred Twine (November 2019) – a randomly generated hypertext version of Moby Dick in Twine, with 2,463 pages and 6,476 links, and utterly impossible to make sense of
Masks (December 2019) – a short hypertext narrative inspired by the Hong Kong protests
@BioDiversityPix (February 2020) – A bot that tweets random illustrations from the Biodiversity Heritage Library
Ring Pandemic Log (April 2020) – Using the same concept of Ring™ Log, this version imagines what a Ring camera might see during an early day of the coronavirus quarantine
You Gen #9 (May 2020) – the first chapter of a longer counterfactual interactive narrative about eugenics and gene-editing technology, set in the 1920s
Content Moderator Sim (June 2020) – A workplace horror game that puts you in the role of a subcontractor whose job is to keep your social media platform safe and respectable.
In general I was working in one of two modes for each project: procedural generation or interactive fiction. The former hopes to surprise readers with serendipitous juxtapositions and combinations, the latter hopes to entice readers with narrative impact. Whether I succeed at either is a question I’ll leave to others.
In 1965 the singer-songwriter Phil Ochs told an audience that “a protest song is a song that’s so specific you can’t mistake it for bullshit.” Ochs was introducing his anti-war anthem “I Ain’t Marching Anymore”—but also taking a jab at his occasional rival Bob Dylan, whose expressionistic lyrics by this time resembled Rimbaud more than Guthrie. The problem with Dylan, as far as Ochs was concerned, wasn’t that he had gone electric. It was that he wasn’t specific. You never really knew what the hell he was singing about. Meanwhile Ochs’ debut album in 1964 was an enthusiastic dash through fourteen very specific songs. The worst submarine disaster in U.S. history. The Cuban Missile Crisis. The murder of Emmett Till, the assassination of Medgar Evers. The sparsely produced album was called All the News That’s Fit to Sing, a play on the New York Times slogan “All the News That’s Fit to Print.” But more than mere parody, the title signals Ochs’ intention to best the newspaper at its own game, pronouncing and denouncing, clarifying and explaining, demanding and indicting the events of the day.
Ochs and the sixties protest movement are far removed from today’s world. There’s the sheer passage of time, of course. But there’s also been a half century of profound social and technological change, the greatest being the rise of computational culture. Networks, databases, videogames, social media. What, in this landscape, is the 21st century equivalent of a protest song? What is the modern version of a song so specific in its details, its condemnation, its anger, that it could not possibly be mistaken for bullshit?
One answer is the protest bot. A computer program that reveals the injustice and inequality of the world and imagines alternatives. A computer program that says who’s to praise and who’s to blame. A computer program that questions how, when, who and why. A computer program whose indictments are so specific you can’t mistake them for bullshit. A computer program that does all this automatically.
Bots are small automated programs that index websites, edit Wikipedia entries, spam users, scrape data from pages, launch denial of service attacks, and other assorted activities, both mundane and nefarious. On Twitter bots are mostly spam, but occasionally, they’re creative endeavors.
The bots in this small creative tribe that get the most attention—the @Horse_ebooks of the world (though @horse_ebooks would of course turn out later not to be a bot)—are surreal, absurd, purposeless for the sake of purposelessness. There is a bot canon forming, and it includes bots like @tofu_product, @TwoHeadlines, @everycolorbot, and @PowerVocabTweet. This emerging bot canon reminds me of the literary canon, because it values a certain kind of bot that generates a certain kind of tweet.
To build on this analogy to literature, I think of Repression and Recovery, Cary Nelson’s 1989 effort to reclaim a strain of American poetry excluded from traditional literary histories of the 20th century. The crux of Nelson’s argument is that there were dozens of progressive writers in the early to mid-20th century whose poems provided inconvenient counter-examples to what was considered “poetic” by mainstream culture. These poems have been left out of the canon because they were not “literary” enough. Nelson accuses literary critics of privileging poems that display ambivalence, inner anguish, and political indecision over ones that are openly polemical. Poems that draw clear distinctions between right and wrong, good and bad, justice and injustice are considered naïve by the academic establishment and deemed not worthy of analysis or teaching, and certainly not worthy of canonization. It’s Dylan over Ochs all over again.
A similar generalization might be made about what is valued in bots. But rather than ambivalence and anguish being the key markers of canon-worthy bots, it’s absurdism, comical juxtaposition, and an exhaustive sensibility (the idea that while a human cannot tweet every word or every unicode character, a machine can). Bots that don’t share these traits—say, a bot that tweets the names of toxic chemicals found in contaminated drinking water or tweets civilian deaths from drone attacks—are likely to be left out of the bot canon.
I don’t care much about the canon, except as a means to clue us in to what stands outside the canon. We should create and pay attention to bots that don’t fit the canon. And protest bots should be among these bots. We need bots that are not (or not merely) funny, random, or comprehensive. We need bots that are the algorithmic equivalent of the Wobblies’ Little Red Songbook, bots that fan the flames of discontent. We need bots of conviction.
Bots of Conviction
In his classic account of the public sphere, that realm of social life in which individuals discuss and shape public opinion, the German sociologist Jürgen Habermas describes a brief historical moment in the early 19th century in which the “journalism of conviction” thrived. The journalism of conviction did not simply compile notices as earlier newspapers had done; nor did the journalism of conviction seek to succeed purely commercially, serving the private interests of its owners or shareholders. Rather, the journalism of conviction was polemical, political, fervently debating the needs of society and the role of the state.
We may have lost the journalism of conviction, but it’s not too late to cultivate bots of conviction. I want to sketch out five characteristics of bots of conviction. I’ll name them here and describe each in more details. Bots of conviction are topical, data-based, cumulative, oppositional, and uncanny.
Topical. Asked where the ideas for his song came from, Ochs once pulled out a Newsweek and smiled, “From out of here.” Though probably apocryphal, the anecdote highlights the topical nature of protest songs, and by extension, protest bots. They are not about lost love or existential anguish. They are about the morning news—and the daily horrors that fail to make it into the news.
Data-based. Bots of conviction are based in data, which is another way of saying they don’t make this shit up. They draw from research, statistics, spreadsheets, databases. Bots have no subconscious, so any imagery they use should be taken literally. Protest bots give witness to the world we inhabit.
Cumulative. It is the nature of bots to do the same thing over and over again, with only slight variation. Repetition with a difference. Any single iteration may be interesting, but it is in the aggregate that a protest bot’s tweets attain power. The repetition builds on itself, the bot relentlessly riffing on its theme, unyielding and overwhelming, a pile-up of wreckage on our screens.
Oppositional. This is where the conviction comes in. Whereas the bot pantheon is populated by l’bot pour l’bot, protest bots take a stand. Society being what it is, this stance will likely be unpopular, perhaps even unnerving. Just as the most affecting protest songs made their audiences feel uncomfortable, bots of conviction challenge us to consider our own complicity in the wrongs of the world.
Uncanny. I’m using uncanny in the Freudian sense here, but without the psychodrama. The uncanny is the return of the repressed. The appearance of that which we had sought to keep hidden. I have to thank Zach Whalen for highlighting this last characteristic, which he frames in terms of visibility. Protests bots often reveal something that was hidden; or conversely, they might purposefully obscure something that had been in plain sight.
It’s one thing to talk about bots of conviction in theory. It’s quite another to talk about them in practice. What does a bot of conviction actually look like?
Consider master botmaker Darius Kazemi’s @TwoHeadlines. On one hand, the bot is most assuredly topical, as it functions by yoking two distinct news headlines into a single, usually comical headline. The bot is obviously data-driven too; the bot scrapes the headline data directly from Google News. On the other hand, @TwoHeadlines is neither cumulative nor oppositional. The bot posts at a moderate pace of once per hour, but while the individual tweets accumulate they do not build up to something. There is no theme the algorithm compulsively revisits. Each tweet is a one-off one-liner. Most critically, though, the bot takes no stance. @TwoHeadlines reflects the news, but it does not reflect on the news. It may very well be Darius’ best bot, but it lacks all conviction.
Vice Provost for Spinoff-o-vation says: Gatesean institution breaks up credit hour badges e-text-booksssss brainzzz
What about another recent bot, Chuck Rybak’s @TheHigherDead? Chuck lampoons utopian ed-tech talk in higher education, putting jargon such as “disrupt” and “innovate” in the mouths of zombies. Chuck uses the affordances of the Twitter bio to sneak in a link to the Clayton Christensen Institute. Christensen is the Harvard Business School professor who popularized terms like “disruptive innovation” and “hybrid innovation”—ideas that when applied to K12 or higher ed appear to be little more than neo-liberal efforts to pare down labor costs and disempower faculty. When these ideas are actually put into action, we get the current crisis in the University of Wisconsin system, where Chuck teaches. @TheHigherDead is oppositional and uncanny, in the way that anything having to do with zombies is uncanny. It’s even topical, but is it a protest bot? It’s parody, but its data is too eclectic to be considered data-based. If @TheHigherDead mined actual news accounts and ed-tech blogs for more jargon and these phrases showed up in the tweets, the bot would rise beyond parody to protest.
@TwoHeadlines and @TheHigherDead are not protest bots, but then, they’re not supposed to be. I am unfairly applying my own criteria to it, but only to illustrate what I mean by the terms topical, data-based, cumulative, oppositional, and uncanny. It’s worth testing this criteria against another bot: Zach Whalen’s @ClearCongress. This bot retweets members of Congress after redacting a portion of the original tweet. The length of the redaction corresponds to the current congressional approval rate; the lower the approval rating, the more characters are blocked.
MT █ SENJOHNTHUNE: ▓▓▓▓▒ ▒▓▓▓▓▓▓▒ ▓▓▒▓ ▓▓▓▓ ▓▓▓▓▓CKET WITH NEW ▒▓▓▓▓▒ ▓▓▓▓▓▒▓▓▓▒▓ @▒▓▒▓▓▒▒ ▓▒▓▓▓▓▒▓▓▓▓▓▓▒▒▓▒▓▓▓▓▒
Assuming our senators and representatives post about current news and policies, the bot is topical. It is also data-driven, doubly-so, since it pulls from congressional accounts and up-to-date polling data from the Huffington Post. The bot is cumulative as well. Scrolling through the timeline you face an indecipherable wall of ▒▒▒▒ and ▓▓▓▓, a visual effect intensified by Twitter’s infinite scrolling. By obscuring text, the bot plays in the register of the visible and invisible—the uncanny. And despite not saying anything legible, @ClearCongress has something to say. It’s an oppositional bot, thematizing the disconnect between the will of the people and the rulers of the land. At the same time, the bot suggests that Congress has replaced substance with white noise, that all senators and representatives end up sounding the same, regardless of their politics, and that, most damning of all, Congress is ineffectual, all but useless.
List of foreign-born United States politicians Wikipedia article edited anonymously from US House of Representatives https://t.co/1CKGIOUn99
Another illustrative protest bot likewise uses Congress as its target. Ed Summers’ @congressedits tweets whenever anonymous edits are made to Wikipedia from IP addresses associated with the U.S. Congress. In other words, whenever anyone in Congress—likely Congressional staffers, but conceivably representatives and senators themselves—attempts to edit a Wikipedia article anonymously, the bot flags that edit and calls attention to it. This is the uncanny hallmark of @congressedits: making visible that which others seek to hide, bringing transparency to a key source of information online, and in the process highlighting the subjective nature of knowledge production in online spaces. @congressedits operates in near real-time; these are not historical revisions to Wikipedia, they are edits that are happening right now. The bot is obviously data-driven too. Summers’ bot responds to data from Wikipedia’s API, but it also send us, the readers, directly to the diff page of that edit, where we can clearly see the specific changes made to the page. It turns out that many of the revisions are copyedits—fixing punctuation, spelling, or grammar. This revelation undercuts our initial cynical assumption that every anonymous Wikipedia edit from Congress is ideologically-driven. Yet it also supports the message of @ClearCongress. Congress is so useless that they have nothing better to do than fix comma splices on Wikipedia? Finally, there’s one more layer of @congressedits to mention, which speaks again to the issue of transparency. Summers has shared the code on Github, making it possible for others to programmatically develop customized clones, and there are dozens of such bots now, tracking changes to Wikipedia.
There are not many bots of conviction, but they are possible, as @ClearCongress and @congress-edits demonstrate. I’ve attempted to make several agit-bots myself, though when I started, I hadn’t thought through the five characteristics I describe above. In a very real sense, my theory about bots as a form of civic engagement grew out of my own creative practice.
I made my first protest bot in the wake of the Snowden revelations about PRISM, the NSA’s downstream surveillance program. I created @NSA_PRISMbot. The bot is an experiment in speculative surveillance, imagining the kind of useless information the NSA might distill from its invasive data-gathering:
Susie Boyle of El Oscarhaven, Montana mentioned “bibliomaniacal” in a chat on Google Hangouts.
@NSA_PRISMbot is topical, of course, rooted in specificity. The Internet companies the bot names are the same services identified on the infamous NSA PowerPoint slide. When Microsoft later changed the name of SkyDrive to OneDrive, the bot even reflected that change. Similarly, @NSA_PRISMbot will occasionally flag (fake) social media activity using the list of keywords and search terms the Department of Homeland Security tracks on social media.
Any single tweet of NSA_PRISMbot may be clever, with humorous juxtapositions at work. But the real power of the bot is the way the individual invasions of privacy accumulate. The bot is like a devotional exercise, in which repetition is an attempt at deeper understanding.
I followed up @NSA_PRISMbot with @NSA_AllStars, whose satirical profile notes that it “honors the heroes behind @NSA_PRISMbot, who keep us safe from the bad guys.” This bot builds on the revelations that NSA workers and subcontractors had spied on their own friends and family.
The bot names names, including the various divisions of the NSA and the companies that are documented subcontractors for the NSA.
A Bot Canon of Anger
While motivated by conviction, neither of these NSA bots are explicit in their outrage. So here’s an angry protest bot, one I made out of raw emotion, a bitter compound of fury and despair. On May 23, 2014, Elliot Rodger killed six people and injured fourteen more near the campus of UC-Santa Barbara. In addition to my own anger I was moved by the grief of my friends, several of whom teach at UC Santa Barbara. It was Alan Liu’s heartfelt act of public bereavement that most clearly articulated what I sought in this protest bot:
What is the literary canon of anger that must back up that of consolation to give full-throated voice to #NotOneMore? →
Whereas Alan turns toward literature for a full-throated cry of anger, I turned toward algorithmic culture, to the margins of the computational world. I created a bot of consolation and conviction that—to paraphrase Phil Ochs in “When I’m Gone”—tweets louder than the guns.
The bot I made is @NRA_Tally. It posts imagined headlines about mass shootings, followed by a fictionalized but believable response from the NRA:
The bot is topical, grievously so. More critically, you cannot mistake it for bullshit. The bot is data-driven, populated with statistics from a database of over thirty years of mass shootings in the U.S. Here are the individual elements that make up the template of every @NRA_Tally tweet:
A number. The bot selects a random number between 4 (the threshold for what the FBI defines as mass murder) and 35 (just above the Virginia Tech massacre, the worst mass shooting in American history).
The victims. The victims are generalizations drawn from the historical record. Sadly this means teachers, college students, elementary school children.
Location. The city and state names have all been sites of mass shootings. I had considered either seeding the location with a huge list of cities or simply generating fake city names (which is what @NSA_PRISMbot does). I decided against these approaches, however, because I was determined to have @NRA_Tally act as a witness to real crimes.
Firearm. The bot randomly selects the deadly weapon from an array of 64 items, all handguns or rifles that have been used in a mass shooting in the United States. An incredible 75% of the weapons fired in mass shootings have been purchased legally, the killers abiding by existing gun regulations. Many of the guns were equipped with high-capacity magazines, again, purchased legally. The 140-character constraint of Twitter means some weapon names have been shortened, dropping, for example the words “semiautomatic” or “sawed-off.”
Response. This is a statement from the NRA in the form of a press release. Every possible response mirrors actual rhetorical moves the NRA has made after previous mass shootings. There are currently 14 stock responses, but the NRA has undoubtedly issued other statements of scapegoating and misdirection. @NRA_Tally is participatory in the sense that you can contribute to its database of responses. Simply submit a generalized yet documented response and I will incorporate it into the code.
@NRA_Tally is terrifying and unsettling, posing scenarios that go beyond the plausible into the realm of the super-real. It is an oppositional bot on several levels. It is obviously antagonistic toward the NRA. It is oppositional toward false claims that “guns don’t kill people,” purposefully foregrounding weapons over killers. It is even oppositional to social media itself, challenging the logic of following and retweeting. Who would be comfortable seeing such tragedies in their timeline on an hourly basis? Who would dare to retweet something that could be taken as legitimate news, thereby spreading unnecessary rumors and lies?
Protest Bots as Tactical Media
A friend who saw an early version of @NRA_Tally expressed unease about it, wondering whether or not the bot would be gratuitous. The bot canon is full of playful bots that are nonsensical and superfluous. @NRA_Tally is neither playful nor nonsensical, but is it superfluous?
No, it is not. @NRA_Tally, like all protest bots, is an example of tactical media. Rita Raley, another friend at UCSB, literally wrote the book on tactical media, a form of media activism that engages in a “micropolitics of disruption, intervention, and education.” Tactical media targets “the next five minutes” rather than some far off revolutionary goal. As tactical media, protest bots do not offer solutions. Instead they create messy moments that destabilize narratives, perspectives, and events.
How might such destabilization work in the case of @NRA_Tally?
As Salon points out, it is the NRA’s strategy—this is a long term policy rather than a tactical maneuver—to shut down debate by accusing anyone who talks about gun control as politicizing the victims’ death. A bot of conviction, however, cannot be shut down by such ironic accusations. A protest bot cannot be accused of dishonoring the victims when there are no actual victims. As the bot inexorably piles on headline after headline, it becomes clear that the center of gravity of each tweet is the name of the weapon itself. The bot is not about victims. It is about guns and the organization that makes such preventable crimes possible.
The public debate about gun violence is severely limited. This bot attempts to unsettle it, just for a minute. And, because this is a bot that doesn’t back down and cannot cower and will tweet for as long as I let it, it has many of these minutes to make use of. Bots of conviction are also bots of persistence.
Adorno once said that it is the role of the cultural critic to present society a bill it cannot pay. Adorno would not have good things to say about computational culture, let alone social media. But even he might appreciate that not only can protest bots present society a bill it cannot pay, they can do so at the rate of once every two minutes. They do not bullshit around.
An earlier version of this essay on Protest Bots can be found on Medium.
The Electronic Literature Organization’s annual conference was last week in Milwaukee. I hated to miss it, but I hated even more the idea of missing my kids’ last days of school here in Madrid, where we’ve been since January.
If I had been at the ELO conference, I’d have no doubt talked about bots. I thought I already said everything I had to say about these small autonomous programs that generate text and images on social media, but like a bot, I just can’t stop.
Here, then, is one more modest attempt to theorize bots—and by extension other forms of computational media. The tl;dr version is that there are two archetypes of bots: closed bots and green bots. And each of these archetypes comes with an array of associated characteristics that deepen our understanding of digital media. Continue reading “Closed Bots and Green Bots”→