After much ado, I was finally able to get a twitterbot up and running. I created a bot @somemodernist that tweets out lines from Virginia Woolf’s novel, Jacob’s Room (1922), whenever I run the module. I chose Jacob’s Room because Woolf wrote the novel with space breaks — that is, she divides the narrative with blank spaces into “scenes” or little sketches. I’ve done work on this novel before (I actually created a prototype for an online edition of the novel for my MA project), and I’m always really inspired by how the digital medium engages with Woolf’s work, in this case, with the fragmentary structure of her narrative and her use of blank spaces.

To make this bot, I used the tutorial from  the “Build a Bot” workshop developed by Terian Koscik. Though I still had difficulty getting the bot up and running, the workshop was extremely helpful for my goal, which was to get a bot to tweet lines in succession from a text file.

That being said, working on this bot actually gave me an idea for something more interesting (unfortunately, @somemodernist isn’t quite as robust as I want it to be, the lines don’t tweet neatly and it won’t run without my prompting). In the near future, maybe over the break, I’m going to make another bot for Woolf’s novel The Waves. For those who are unfamiliar with this novel, the narrative runs through the stream-of-conscious of six different characters, and their thoughts at times share the same phrases and images. For that reason, there’s an interesting literature on Woolf’s creation of a shared consciousness, if any of you guys are interested.  In making this bot, I’m going to get more deeply into Python (which is something I’ve been meaning to do, now I have an excuse!). My goal is to run a script that finds patterns of words or phrases throughout the novel and tweets them in succession, regardless of the speaker. The tweets will then facilitate more study on the shared language of the six characters in the novel.

I found some resources that will help with this kind of text analysis in Python. First there’s the Natural Language Processing with Python (NLTK) Book, which is an online book that teaches beginners how to do text analysis in Python. Then there’s the pattern.en module, which allows for more advanced syntactic searches and analysis, and finally there’s the TextBlob module, which is like a more beginner-friendly library of scripts for processing text.

For the past several weeks, I’ve been thinking about computer code and language as a means of control. For Alexander Galloway, code is a type of protocol, or a way of imposing control in communication. In Protocol, Galloway describes code as a language (yet to be officially recognized as such) that requires adherence to its standards in order to work. For Kenneth Goldsmith, code offers an opportunity to be creative. In Uncreative Writing, Goldsmith talks about remixing different kinds of “code,” such as the code from an image file with lines of poetry, to create a new image. I’m wondering how we can bring what Galloway says about control and restraint in code into conversation with Goldsmith’s presentation of creative uses of code.

Galloway shows how power structures, such as DNS and ISP, are instituted through code, which must conform to a certain standard in order to successfully communicate. For Galloway, resistance to this kind of control consists of finding loopholes or “exploits” in systems (what hackers do). But Goldsmith shows how resistance to standards can take a different route, how it can actually defy the requirements of protocol, by splicing the standard code with other kinds of code, or languages. This remixing is creative because it combines two different codes (such as poetry and computer code) to create something new.

On page 24 in Uncreative Writing. Goldsmith performs an experiment with an image of William Shakespeare. His experiment takes the textual code from the image and splices it with the text from a Shakespearean sonnet. The resulting .jpg file renders a jumbled image. I performed the same experiment with a picture of my family’s thanksgiving table. Here, I took the code from a .jpg file and spliced it with text (in this case, with an argument that my family had at the table when the picture was taken). The result looks like this:

The image shows two things: first, how the code doesn’t work, and second, how this failure nonetheless results in an image that is read and rendered by the computer. In this case, the remixing of code is both corruptive and creative. It shows that mixing different kinds of languages, such as English and computer code, successfully resists standards.

I realize that Galloway’s project is ultimately about communication, while Goldsmith’s is about creating something new from old materials. But it seems to me that we can see the two in the same light, as a resistance to the control of language through experimentation. Do you think this experiment changes how we view protocol? Is mixing different kinds of code analogous to Galloway’s “exploit”, or is it something else?

On Tuesday, after our visit to the datacenter, Andrew Blum made a comment about stories that made me think back to our discussions of “mapping” networks. I can’t remember his exact words, but Blum was talking about the way that our guides presented the data center to us, saying that people who are immersed in that kind of work often don’t tell the story that we want to hear. His comment led me to another comment from the introduction to his book, Tubes, in the section where he discusses the “folk cartography” of Kevin Kelly. Blum explains how Kelly solicited the public to submit hand-drawn maps of their conceptions of the internet (“Internet Mapping Project”). A researcher, Mara Vanina Oses, from the University of Buenos Aires, analyzed these maps by their topologies (here’s her presentation) . Blum notes that while these maps are perceptive, displaying our awareness of our experience in the network, none of them actually reference the physical machines and tubes that make up the network. And he rightly points out that ignorance about how these infrastructures actually work is dangerous: “the great global scourges of modern life are always made worse by not knowing. Yet we treat the Internet as if it were a fantasy” (7).

I see a similar formulation from Deleuze and Guattari, who make a distinction between mapping and tracing. According to these theorists, tracing only reinforces what we already know, what is in the unconscious, while a map attempts to engage with the real:

“The tracing has already translated the map into an image ; it has already transformed the rhizome into roots and radicles. It has organized, stabilized, neutralized the multiplicities according to the axes of significance and subjectification belonging to it. It has generated, structuralized the rhizome, and when it thinks it is reproducing something else it is in fact only reproducing itself. That is why the tracing is so dangerous. It injects redundancies and propagates them” (A Thousand Plateaus 13).

There’s an analogy where we can associate Kelly’s “folk cartography” with D&G’s tracing, while the investigative work that Blum undertakes is like D&G’s mapping. All three theorists think that this tracing activity is potentially dangerous, because it neglects the workings of the actual infrastructure that exists outside the mind. But I think there’s something to be learned by looking at these citizen “tracings” of the network. While Blum rightly points out that these tracings are fantasies, they reveal the aesthetic experience of being on the network, and how certain users tend to sense, at least on an abstract level, scope of the system.

Although the maps from the “Internet Mapping Project” don’t explicitly indicate the cables and servers that make up the internet, they do reveal the sense of obscurity and incomprehensibility about the network. In their furious, numerous lines, or simple, abstract shapes, the users struggle to represent the connectivity of information. While people generally put themselves in the center of their own networks, meaning that they see the network as organized around their needs, they still suggest that the system is complicated, overwhelming, and in many ways beyond them.

We can use these maps to reframe other topics we’ve discussed throughout the semester. What do they have to say about labor, conceptions of labor, or media and agency? I do think these maps deserve a second look because they tell stories about how people experience the internet. It may not be the full story, but it’s worth listening.

In light of our last class’s theme of labor, I thought I would offer some interesting examples of crowdsourced labor that challenge the boundaries between work and play, and public and private. In these examples, I’m interested in how productive leisure activity may be considered “fan labor”, what users get out of such labor, and whether we can consider this labor a personal appropriation of the object. Both of the examples below use crowdsourcing to contribute to literary archives by inviting volunteers to complete transcriptions of written documents.

If we can consider this crowdsourced transcribing activity akin to what Abigail de Kosnik calls “fan labor” in her article, “Fandom as Free Labor”, I’m wondering about the incentive for such labor. How do these volunteers feel enfranchised in their transcription work? And how does their enfranchisement in public, archival work engage with the process of appropriation and customization that de Kosnik describes happens in fan communities?

The first example comes from the Smithsonian. This project allows the average member of the public to engage in archival work by transcribing written documents into print. The website offers a quick tutorial to get volunteers started, and imposes a peer review system to double check the transcriptions. Here, engagement seems to be the primary goal: this is clearly a space for the public, not researchers; there is a low barrier to entry; and participants engage with one another through peer review. Users can just jump from transcription to transcription at will.

The second example comes from University College London, and it’s a “collaborative transcription initiative” that grants digital access to Jeremy Bentham’s unpublished manuscripts. This project requires more experience than the Smithsonian one, as users have to learn how to encode their transcriptions according to UCL’s markup guidelines and create an account before getting started. Despite the higher learning curve, this project has over 30,000 registered users, with almost half of Betham’s folios already transcribed.

These two projects’ methodologies reveal a new type of “fandom.” First, there is the difference in target audiences, then there is how each audience engages with the “product” — the personal process of transcribing the documents. While the Smithsonian project invites all levels of contributors for transcriptions on various subjects, the Bentham project involves a certain understanding of encoding and a special interest in Bentham. Furthermore, all transcriptions in the Bentham project are verified by the paid staff, while the Smithsonian uses a system of public peer review. It seems like UCL’s main audience may be more serious, academically-inclined or interested in the digital humanities, while the Smithsonian is trying to engage with a wider public. Despite this difference in audience, both institutions make users feel enfranchised in the process, perhaps wanting to discuss the text, or feeling a part of it in some way. We can regard these users’ transcriptions as a kind of inverted version of de Kosnik’s “work of customization” that fans undertake when they make something private out of something public (102). Instead of appropriating mass produced objects, these fans work to make the personal widely accessible to the public. Nonetheless, as they carry out their transcriptions, they become a part of the process, and their transcriptions become a kind of appropriation. In a sense, their work of “customization” is to invest the documents with their labor. It would be interesting to look more deeply into these fans to learn more about their relationship to the products of their labor.