A protest bot is a bot so specific you can’t mistake it for bullshit

A Call for Bots of Conviction

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.

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.

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.

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:

@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.

Javascript of @nsa_allstars
The code of @nsa_allstars

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:

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:

Tweet from @NRA_Tally

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:

  1. 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).
  2. The victims. The victims are generalizations drawn from the historical record. Sadly this means teachers, college students, elementary school children.
  3. 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.
  4. 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.”
  5. 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.

Closed Bots and Green Bots
Two Archetypes of Computational Media

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

Followup to the Ever-Expanding Classroom Discussion

Last week I was a guest of the Davidson College Teaching Discussion Group, where I was invited to talk about my pedagogical strategies for teaching large classes. I mostly focused on how I use technology to preserve what I value most about teaching smaller classes. But many of the technique I discussed are equally applicable to any class, of any size.

For participants in the discussion group (and anyone else who is interested), I’ve rounded up a few of my ProfHacker posts, in which I describe in greater detail how I incorporate technologies like blogging and Twitter into my courses.



[Crowd photograph courtesy of Flickr user Michael Dornbierer / Creative Commons Licensed]

Twittering N+7

Magentic Poetry At the risk of alienating my readers on Twitter—something I’m likely to be doing anyway—I’ve been playing an old Oulipo game with my tweets today: N+7. It’s quite simple: replace every noun in a text with the noun that follows it seven nouns later in the dictionary. The results are often nonsensical, occasionally revelatory, and always evocative.

I began by N+7ifying yesterday’s tweets in reverse chronological order (avoiding tweets with @ replies for some reason). A few tweets in, I switched over to N+7ifying my most popular tweets of the past few months, as measured by the number of retweets or replies the status update had. I’ve been doing this all day, and I’ve now got two dozen or so bizarre revisions of earlier tweets.

Why do this?

Isn’t the answer obvious?

I had nothing else to say.

You could call it boredom. Or more generously, writer’s block. Whatever you call it, this fact remains: when you have nothing left to say, artificial constraints and deterministic algorithms will give you something new to say. Boredom leads to constraints, which leads to creativity. This is the nature of play. This is the nature of language. This is the nature of meaning.

Magnetic Poetry image courtesy of Flickr user surrealmuse / Creative Commons License]

Twitter is a Happening, to which I am Returning

I quit Twitter.

White Noise and Static

Or, more accurately, I quit twittering. Nearly three weeks ago with no warning to myself or others, I stopped posting on Twitter. I stopped updating Facebook, stopped checking in on Gowalla, stopped being present. I went underground, as far underground as somebody whose whole life is online can go underground.

In three years I had racked up nearly 9,000 tweets. If Twitter were a drug, I’d be diagnosed as a heavy user, posting dozens of times a day. And then I stopped.

Most people probably didn’t notice. A few did. I know that they noticed because my break from social media wasn’t complete. I lurked, intently, in all of these virtual places, most intently on Twitter.

White Noise at 10 Percent

In the weeks I was silent on Twitter I read in my timeline about divorce, disease, death. I read hundreds of tweets about nothing at all. I read tweets about scholarship, about teaching, about grading, about sleeping and not sleeping. Tweets about eating. Tweets about me. Tweets with questions and tweets with answers. And I thought about how I use Twitter, what it means to me, what it means to share my triumphs and my frustrations, my snark and my occasional kindness, my experiments with Twitter itself.

White Noise Static at 20 Percent Opacity

For the longest time the mantra “Blog to reflect, Tweet to connect” was how I thought about Twitter. The origin of that slogan is blogger Barbara Ganley, who was quoted two years ago in a New York Times article on slow blogging. Ganley’s pithy analysis seemed to summarize the difference between blogging and Twitter, and it circulated widely among my friends in the digital humanities. I repeated the slogan myself, even arguing that Twitter was the back channel for the digital humanities, an informal network—the informal network—that connected the graduate students, researchers, teachers, programmers, journalists, librarians, and archivists who work where technology and the humanities meet.

White Noise Static at 30 Percent Opacity

My retreat from Twitter has convinced me, however, that Twitter is not about connections. Saying that you tweet in order to connect is like saying you fly on airplanes in order to get pat-down by the TSA. If you’re looking for connections on Twitter, then you’re in the wrong place. And any connections you do happen to form will be random, accidental, haunted by mixed signals and potential humiliations.

I’ve been mulling over a different slogan in my mind. One that captures the multiplicity of Twitter. One that acknowledges the dynamism of Twitter. One that better describes my own antagonistic use of the platform. And it’s this:

Blogging is working through. Twitter is acting out.

White Noise Static at 40 Percent Opacity

Twitter is not about connections. Twitter is about acting out.

I mean “working through” and “acting out” in several ways. There’s the obvious allusion to Freud: working through and acting out roughly correspond to Freud’s distinction between mourning and melancholy. A mourner works through the past, absorbs it, integrates it. A mourner will think about the past, but live into the present. The melancholic meanwhile is prone to repetition, revisiting the same traumatic memory, replaying variations of it over and over. The melancholic lashes out, sometimes aggressively, sometimes defensively, often unknowingly.

It’s not difficult to see my use of Twitter as acting out, as rehashing my obsessions and dwelling upon my contentions. Even my break from Twitter is a kind of acting out, a passive-aggressive refusal to play.

But I also mean “acting out” in a more theatrical sense. Acting. Twitter is a performance. On my blog I have readers. But on Twitter I have an audience.

White Noise Static at 50 Percent Opacity

To be sure, it’s a participatory audience. Or at least possibly participatory. And this leads me to another realization about Twitter:

Twitter is a Happening.

I’m using Happening in the sixties New York City art scene sense of the word: an essentially spontaneous artistic event that stands outside—or explodes from within—the formal spaces where creativity is typically safely consumed. Galleries, stages, museums. As Allan Kaprow, one of the founders of the movement, put it in 1961,

[quote]Happenings are events that, put simply, happen. Though the best of them have a decided impact—that is, we feel, “here is something important”—they appear to go nowhere and do not make any particular literary point.[/quote]

Happenings lack any clear divide between the audience and the performers. Happenings are emergent, generated from the flimsiest of intentions. Happenings cannot be measured in terms of success, because even when they go wrong, they have gone right. Chance reigns supreme, and if a Happening can be reproduced, reenacted, it is no longer a happening. And if it’s not a Happening, then nothing happened.

White Noise Static at 60 Percent Opacity

Whether it’s a Twitter-only mock conference, ridiculous fake direct messages, or absurd tips making fun of our professional tendencies, I have insisted time and time again—though without consciously framing it this way—that Twitter ought to be a space for Happenings.

If you’re not involved somehow in a Twitter Happening—if you’re not inching toward participating in some spontaneous communal outburst of analysis or creativity—then you might as well switch to Facebook for making your connections.

Because Twitter is a Happening that thrives on participation, there’s something else I’ve realized about Twitter:

Twitter is better when I’m tweeting.

White Noise Static at 70 percent OpacityIf you are one of the nearly four hundred people I follow, don’t take this the wrong way, but Twitter is better when I’m around. I don’t mean to say that the rest of you are uninteresting. But until I or a few other like-minded people in my Twitter stream do something unexpected, Twitter feels flat, a polite conversation that may well be informative but is nothing that will leave me wondering at the end of the day, what the hell just happened?

I suppose this sounds arrogant. “Twitter is better when I’m around”?? I mean, who on earth made me judge of all of Twitterdom?? And indeed, this entire blog post likely seems self-indulgent. But I didn’t write it for you. I wrote it for me. I’m working through here. And besides, I’ve been criticized too many times by the people who know me best in real life, criticized for being too modest, too eager to downplay my own voice, that I’ll risk this one time sounding self-important.

There’s one final realization I’ve had about Twitter. For a while I had been wondering whether every word I wrote on Twitter was one less word I would write somewhere else. Was Twitter distracting me from what I really needed to write? Was Twitter making me less prolific? And so here it is, my most coherent articulation of what led me to break suddenly from social media: I quit Twitter because I wished to write deliberately, to type only the essential words of my research, and see if I could not learn what Twitterless life had to teach, and not, when I came up for tenure, discover that I had not written at all.

Or something like that.

It only took a few days before I knew the answer to my question about Twitter and writing. And it’s this: writing is not a zero sum game.

I write more when I tweet.

This is not as self-evident a truth as it sounds. Obviously every tweet means I’ve written everything I’ve ever written in my life, plus that one additional tweet. So yes, by tweeting I have written more. But in fact I write more of everything when I tweet. I have learned in the past few weeks that Twitter is a multiplier. Twitter is generative. Twitter is an engine of words, and when I tweet, all my writing, offline and on, private and public, benefits. There’s more of it, and it’s better.

And so I am returning to Twitter. While I had experimented with tweeterish postcards during my break from Twitter—what you might call slow tweeting—I am back on Twitter, and back for good. Twitter is a Happening. It’s not a space for connections, it’s a space for composition. I invite you to unfollow me if you think differently, for I can promise nothing about what I will or will not tweet and with what frequency these tweets will or will not come. I would also invite you to the Happening on Twitter, but that invitation is not mine to extend. It belongs to no one and to everyone. It belongs to the crowd.

White Noise and Static

Maps and Timelines

Over a period of a few days last week I posted a series of updates onto Twitter that, taken together, added up to less than twenty words. I dragged out across fourteen tweets what could easily fit within one. And instead of text alone, I relied on a combination words and images. I’m calling this elongated, distributed form of social media artisanal tweeting. Maybe you could call it slow tweeting. I think some of my readers simply called it frustrating or even worthless.

If you missed the original sequence of updates as they unfolded online, you can approximate the experience in this thinly annotated chronological trail.

I’m not yet ready to discuss the layers of meaning I was attempting to evoke, but I am ready to piece the whole thing together—which, as befits my theme, actually destroys much of the original meaning. Nonetheless, here it is:

One Week, One Tool, Many Anthologies

Many of you have already heard about Anthologize, the blog-to-book publishing tool created in one week by a crack team of twelve digital humanists, funded by the NEH’s Office of Digital Humanities, and shepherded by George Mason University’s Center for History and New Media. Until the moment of the tool’s unveiling on Tuesday, August 3, very few people knew what the tool was going to be. That would include me.

So, it was entirely coincidental that the night before Anthologize’s release, I tweeted:

I had no idea that the One Week Team was working on a WordPress plugin that could take our blogs and turn them into formats suitable for e-readers or publishers like Lulu.com (the exportable formats include ePub, PDF, RTF, and TEI…so far). When I got a sneak preview of Anthologize via the outreach team’s press kit, it was only natural that I revisit my previous night’s tweet, with this update:

I’m willing to stand behind this statement—Twitter and Blogs are the first drafts of scholarship. All they need are better binding—and I’m even more willing to argue that Anthologize can provide that binding.

But the genius of Anthologize isn’t that it lets you turn blog posts into PDFs. They are already many ways to do this. The genius of the tool is the way it lets you remix a blog into a bound object. A quick look at the manage project page (larger image) will show how this works:

All of your blog’s posts are listed in the left column, and you can filter them by tag or category. Then you drag-and-drop specific posts into the “Parts” column on the right side of the page. Think of each Part as a separate section or chapter of your final anthology. You can easily create new parts, and rearrange the parts and posts until you’ve found the order you’re looking for.

Using the “Import Content” tool that’s built into Anthologize, you aren’t even limited to your own blog postings. You can import anything that has an RSS feed, from Twitter updates to feeds from entirely different blogs and blogging platforms (such as Movable Type or Blogger). You can remix from a countless number of sources, and then compile it all together into one slick file. This remixing isn’t simply an afterthought of Anthologize. It defines the plugin and has enormous potential for scholars and teachers alike, ranging from organizing tenure material to building student portfolios.

Something else that’s neat about how Anthologize pulls in content is that draft (i.e. unpublished) posts show up alongside published posts in the left hand column. In other words, drafts can be published in your Anthologize project, even if they were never actually published on your blog. This feature makes it possible to create Anthologize projects without even making the content public first (though why would you want to?).

From Alpha to Beta to You

As excited as I am about the possibilities of Anthologize, don’t be misled into thinking that the tool is a ready-to-go, full-fledged publishing solution. Make no mistake about Anthologize: this is an extremely alpha version of the final plugin. If the Greeks had a letter that came before alpha, Anthologize would be it. There are several major known issues, and there are many features yet to add. But don’t forget: Anthologize was developed in under 200 hours. There were no months-long team meetings, no protracted management decisions, no obscene Gantt charts. The team behind Anthologize came and saw and coded, from brainstorm to repository in one week.
[pullquote align=”left”]The team behind Anthologize came and saw and coded, from brainstorm to repository in one week.[/pullquote] The week is over, and they’re still working, but now it’s your turn too. Try it out, and let the team know what works, what doesn’t, what you might use it for, and what you’d like to see in the next version. There’s an Anthologize Users Group you can join to share with other users and the official outreach team, and there’s also the Anthologize Development Group, where you can share your bugs and issues directly with the development team.

As for me, I’m already working on a wishlist of what I’d like to see in Anthologize. Here are just a few thoughts:

  • More use of metadata. I imagine future releases will allow user-selected metadata to be included in the Anthologized content. For example, it’d be great to have the option of including the original publication date.
  • Cover images. It’s already possible to include custom acknowledgments and dedications in the opening pages of the Anthologized project, but it’ll be crucial to be able to include a custom image as the anthology front cover.
  • Preservation of formatting. Right now quite a bit of formatting is stripped away when posts are anthologized. Block quotes, for example, become indistinguishable from the rest of the text, as do many headers and titles.
  • Fine-grained image control. A major bug prevents many blog post images from showing up in the Anthologize-generated book. Once this is fixed, it’d be wonderful to have even greater control of images (such as image resolution, alignment, and captions).
  • I haven’t experimented with Anthologize on WordPressMU or BuddyPress yet, but it’s a natural fit. Imagine each user being able to cull through tons of posts on a multi-user blog, and publishing a custom-made portfolio, comprised of posts that come from different users and different blogs.

As I play with Anthologize, talk with the developers, and share with other users, I’m sure I’ll come up with more suggestions for features, as well as more ways Anthologize can be used right now, as is. I encourage you to do the same. You’ll join a growing contingent of researchers, teachers, archivists, librarians, and students who are part of an open-source movement, but more importantly, part of a movement to change the very nature of how we construct and share knowledge in the 21st century.