Moth Generator (@mothgenerator) is an interactive, multi-faceted, collaborative digital artwork by Katie Rose Pipkin and Loren Schmidt. The following statements illustrate its complexity and set the stage for an eventual preservation plan for this work:
Moth Generator is:
- A Twitter feed where moths are regularly published and @replies are used as moth-generating text
- A collection of computer-generated moth images and names, including looping animations created from generated moths and reused for other purposes
- An element of a complex virtual world project
- A collaboration between a game designer and an artist whose work deals in large part with code and bots
- A relatively well-known example of a Twitter bot
what is a bot?
To better understand Moth Generator in context, we might begin with a Wikipedia summary: A bot is a “software application that runs automated tasks (scripts) over the Internet.”
Woolley et al. (2016) point to the ubiquity of bots performing invisible work on which the internet runs, and also to “an emergent kind of bot, capable of interacting with humans and acting on their behalf, [that] is playing a more active role in our everyday lives.” Of these, social bots (or socialbots, as this New York Times article spells it) are some of the most visible thanks to their interactions with users within the constraints of social media platforms. Twitter bots are a specific instance of social bot: Twitter accounts that publish posts and/or interact with users based on running certain kinds of inputs through programs.
Twitter bots come in many forms, offering social and political commentary (often accidental), absurd juxtapositions, and variations on jokes, to give a few examples. As Woolley et al. write, “Bots can be useful for making value systems apparent, revealing obfuscated information, and amplifying the visibility of marginalized topics or communities.” Protest bots (per Mark Sample) certainly do this, but so might art bots not explicitly dedicated to social commentary. Describing some of her bot work, Pipkin has said:
Looking at my source code for those projects now is very much like looking at an outsider-art approach to computer science. Which is, I suppose, what they are.
When asked in an interview, “What, indeed, are bots?” Pipkin pointed to their descent from other forms of automata: “Digital bots (especially bots that live in social spaces) fit into this long history of objects granted almost-humanness. They fill in for a part of human action, the slice of person granted to digital representations of ourselves.” Woolley et al. write, “One distinguishing feature of bots is that they are semi-autonomous: they exhibit behavior that is partially a function of the intentions that a programmer builds into them, and partially a function of algorithms and machine learning abilities that respond to a plenitude of inputs.” Bots and their designers perpetually navigate the boundary between intent and accident, including ethical issues involved in giving bots more or less free rein.
Woolley et al. are clear about the upsides to the thoughtful use and study of bots, even as they call (in their “bot-ifesto”) for critical attention to and policy-building around them: “By embracing messiness, those who make, use, and interact with bots open themselves to the automated creativity, innovation, and unpredictability so central to the web.” That Pipkin and Schmidt operate in this emerging space contributes to the significance of Moth Generator as an ongoing work of digital art.
how does @mothgenerator work?
Text input for moth creation has multiple sources. The bot periodically generates and tweets moths whose names are randomly pulled and combined from a set of about 10,000 Latin moth names and 4,000 English names, all real. Pipkin used a web crawler to build the two name pools. An example is below (and more appear throughout the post):
bopteryx hodgannascus pic.twitter.com/7pLAkAK0w2
— moth generator (@mothgenerator) March 5, 2016
Another extremely compelling source of input is tweets from other users. The artists have programmed Moth Generator to use @replies to @mothgenerator to “seed” new moths. A recent example is below:
— moth generator (@mothgenerator) March 19, 2016
stakeholders and significance
Assessing and unpacking the significance of Moth Generator requires identifying major stakeholders in its long-term preservation. What each stakeholder values about Moth Generator must be considered — and negotiated between — in order to produce, execute, review, and update a preservation plan.
Pipkin and Schmidt, the artists behind the bot, are the primary stakeholders in preserving Moth Generator. It’s possible to draw out key concerns from interviews, talks, and blog posts about Twitter bots in general and this project in particular.
Although this project is typically referred to as a Twitter bot, Pipkin has described Moth Generator as a drawing process for which Twitter acts as publishing platform (interview by Fader, 2015). In other words, the drawing program at the heart of Moth Generator is the key component, and @mothgenerator is just one of many possible instances of its implementation. As further evidence of the drawing program’s importance, Schmidt emphasized a link between Moth Generator and entomological illustration in an interview: “Though our own moths are digital, the way they are constructed has more in keeping with a drawn moth than a photograph, and this was an important touchstone” (Nguyen). This connection is expressed not only in the generation of moth shapes from many thousands of marks, but also in how constraints and rules for how moths can look are continually adjusted. Strict adherence to real moth shapes and textures are not the goal.
The bot’s relative autonomy — outside of occasional tweaks to the drawing program and a collection of error screenshots — is another key characteristic from the artists’ perspective. Pipkin has said that she does not monitor her many Twitter bot accounts: “This means that their notifications never reach me; the things that are said to them (or their own replies) are often invisible to everyone but them” (interview with Bucher, 2015). Users interact with the bots and sometimes with each other, their conversations tied to bot tweets through Twitter’s data structures. Interactivity contributes to this project’s significance, not only in the field of social bots but also within the artists’ respective bodies of work. Pipkin has said, “I’m proud of the project in general, but the reason it is my favorite is because it is my first bot that uses the @-reply functionality of Twitter in a constructive way.” The artists may write the rules of the drawing program, but “bots also have their own secret lives outside of intention.”
In addition to being a process with a life of its own, this Twitter bot fall within what Pipkin calls work-by-generation: “a fundamentally similar, but shifted process from that of work-by-hand; rather than identifying and chasing the qualities of a singular desired artwork, one instead defines ranges of interesting permutations, their interpersonal interactions, how one ruleset speaks to one another.” This echoes a frequent argument within the field of software and digital art preservation — or anywhere source code is in play — that preserving potential is a key function of documenting and preserving. To Pipkin and Schmidt, Moth Generator is as much the potential for new moths as it is a collection of them. Pipkin has hinted at moving away from this kind of visible, published output in her experiments with bots: “I have an interest in biological emulation and in the hidden data that Twitter links to every tweet (perhaps my next bot will not be readable on the Twitter webclient, but instead comes alive in an API call?)” Preserving Moth Generator’s potential may mean capturing the kind of underlying Twitter data that allows Pipkin to reuse and remix her own work in the future.
Finally, it’s important to note that Moth Generator was not meant to be a standalone program or bot. Schmidt and Pipkin created it as one element of a larger virtual world project based on “wandering through a generative landscape of interaction” (Levy). They conceived of the virtual world as a second installment of a web-based project called inflorescence.city in which texts and map-like images regenerate each time a URL is refreshed. Moth Generator’s embeddedness should be reflected in any preservation plan.
“Community” is an admittedly broad term for a group comprising audience, co-creators, researchers, and critics. I’m inclined to group them together because, while their precise expectations of Moth Generator are bound to vary, what they value about the project seems to overlap quite a bit.
Moth Generator’s audience includes Twitter users who follow and/or interact with @mothgenerator. Those who follow @mothgenerator on Twitter may do so because they enjoy seeing new moths on the regular. @mothgenerator’s context in a Twitter timeline makes the occasional moth an unexpectedly lovely moment amid news, networking, arguing, and other normal Twitter activities. Many of the articles, interviews, and blog posts about this particular bot note the beauty and weirdness of the moth images and names.
Expectations of these stakeholders may include the ability to @-reply Moth Generator and receive their very own custom moth in response, as well as playing around with wording to generate dramatically or subtly different moths. Other forms of interactivity include sharing moths with others by @-replying other Twitter users, retweeting or favorite-ing @mothgenerator tweets, and potentially even interacting with other users who also reply to or share a moth. For Moth Generator’s audience, Twitter’s interface and functionality may be central to its significance. Given recent uproar about potential changes to Twitter’s 140-character limit and the introduction of a timeline sorted by algorithm (not chronology), capturing some representation of how @mothgenerator currently appears is important. While the character limit change was ultimately rejected, it’s hard to predict how future tweaks to Twitter’s appearance and functionality will affect the experience of creating and interacting with Twitter art. A preservation strategy for Moth Generator will help document this particular moment in bot-making and Internet art.
Finally, it’s possible to see Moth Generator as a digital collection, since what users disseminate when they share a @mothgenerator tweet includes an image and a short bit of text. It’s an unruly and distributed collection, however. Still images produced by Moth Generator may exist in many different places online, including all over Tumblr and in this animated iPhone background.
Pipkin wrote in a post on Medium:
A digital object appears across a multiplicity of screens both at once and forever … There is no distinction between original and copy; they exist at the same scale, have the same aura.
This is debatable as a reality, if not as an artistic approach. We’ve seen over and over again that metadata — what allows this connectivity and shared aura to persist — does not seem inclined to perpetuate or replicate itself. Deliberately collecting, describing, and monitoring source code, documentation, and actual tweet data for long-term preservation will go a long way in supporting researchers studying Twitter art and artists remixing Moth Generator in some way. Pipkin and Schmidt value the autonomy of their bot, but they also seem to trust in its ability to operate indefinitely. Planned obsolescence, glitching, and decay don’t seem to figure in their vision of Moth Generator’s future, so there’s no reason at the moment to think that Moth Generator should not be preserved.
Moth Generator is not only a drawing process, a virtual world feature, a Twitter bot, or a collection of digital images; it is all of the above at once. Assessing the significance of this project means recognizing how its components and manifestations interact and designing a preservation strategy around those interactions.
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Dewey, C. (2016, January 6). Twitter’s rumored character-limit change is bad news for artists and botmakers. Washington Post.
Fader, L. (2015, November 9). 12 weird, excellent Twitter bots chosen by Twitter’s best bot-makers. New York.
Irwin, M. (2013, August 9). Techno-artistic: Katie Rose Pipkin and the new art. The Austin Chronicle.
Levy, L. (2015, August 5). How to make a moth. Studio 360.
Mears, J. (2015, March 29). All about that bot. Digital Public History.
Nguyen, C. (2015, July 16). This Twitter bot automatically generates imaginary moths. Motherboard.
Pipkin, K. R. (2016, March 12). Selfhood, the icon, and byzantine presence on the Internet. Medium.
Sample, M. (2015, October 3). A protest bot is a bot so specific you can’t mistake it for bullshit. samplereality.
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Williams, J. (2015, September 9). Q+A with Katie Rose Pipkin. University of Texas at Austin Department of Art and Art History.
Woolley, boyd, Broussard, Elish, Fader, Hwang, et al. (2016, February 23). How to think about bots. Motherboard.