Final Project: Digital Textual Analysis

Overview

My name is Ben Smith – I am a first-year PhD student in English at Brown. This is a final project for Digital Storytelling, AMST 2699, Spring Semester 2017. It focuses on digital textual analysis. It is an extension of another project I conducted earlier in the semester on Voyant: https://digitalstorytelling.jimmcgrath.us/uncategorized/voyant-read-this-for-me/.

Debates, Major Issues, and Points of Contention for Digital Textual Interpretation

In an article for the LA Review of Books, David Golumbia spells out what seems to me to be the basic point of contention surrounding digital textual interpretation in particular, and Digital Humanities in general:

Advocates position Digital Humanities as a corrective to the “traditional” and outmoded approaches to literary study that supposedly plague English departments. Like much of the rhetoric surrounding Silicon Valley today, this discourse sees technological innovation as an end in itself and equates the development of disruptive business models with political progress. Yet despite the aggressive promotion of Digital Humanities as a radical insurgency, its institutional success has for the most part involved the displacement of politically progressive humanities scholarship and activism in favor of the manufacture of digital tools and archives.

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#Resistance: A Final Project Statement

The #Resistance final project documents the “rogue” government Twitter Resistance movement that to respond to the Trump administration’s social media gag order on federal agencies. Using Documenting the Now’s twarc tool, this project reviews the activity of nine accounts in regards to their conversations, engagement, and language in parodying a government agency. Talking about everything from climate change to internal affairs of the White House, these accounts have played an important role in the coverage of the first 100 days of the Trump administration. This project seeks to document aspects of this coverage by exploring how #Resistance defines itself on Twitter.

What can we learn from “rogue” Twitter about digital activism? What can this movement teach us about advocacy and government in digital public spaces? And where do these two ideas fit into the larger aims of Twitter culture – as noise, or as something greater?

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Big, Weird, and Pretty: Visualizing HAM Museum Data

For our curation assignment, we explored Palladio, RawGraphs, and Tableau to reflect upon how these platforms could visualize our research questions for the Harvard Art Museums’ API. We asked probing questions about the collection’s provenance and provenience, including:

  1. What is the relationship among accession year, date of first pageview, and last pageview?
  2. What is the relationship among accession year, culture, and department?
  3. What is the relationship between total unique pageviews and culture?
  4. What is the relationship between exhibition count and culture?  

The goals of this assignment were to (1) compare different visualization platforms, (2) gather big picture information about the collection’s provenance and provenience, and (3) identify objects in the collection that could be useful for our final digital storytelling project. This blog post recounts our reflections on the first objective, while our final digital storytelling project will feature what we learned in pursuit of the second and third objectives.

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Exploring the Activist Possibilities of Expanded Metadata

“Perhaps those of us who are interested in seeing more robust cultural critique need to be more specific about where the intervention might most productively take place. It is not only about shifting the focus of projects so that they feature marginalized communities more prominently; it is about ripping apart and rebuilding the machinery of the archive and database so that it does not reproduce the logic that got us here in the first place.”

— Miriam Posner, “What’s Next: The Radical, Unrealized Potential of Digital Humanities”

My project started with mangos.

“Those are mangos,” my Museum Interpretation classmate said, pointing to the large painting in front of us with the label covered.

“No, they’re not,” I said reflexively. “Consider where we are.”

We were in the Modern and Contemporary Gallery at the RISD Museum and it turned out we were standing in front of Wifredo Lam’s The Eternal Presence.

The memory of gripping peeled mangos with my tiny hands, feeling the juice running between my fingers and the tiny strands of fiber in my teeth, is one of my oldest. Long before I ever knew what fine art or a museum was, I ate mangos. Yet, encountering a motif from my past in a place like the RISD Museum did not make sense–or shall I say, being Latina did not prevent me from experiencing implicit bias toward an artwork by a Latin American painter.

Shortly after this experience, I was inspired by an internship description at the Smithsonian Latino Center that proposed identifying and tagging objects within their collections that were made by Latinxs. This succession of experiences made me think about performing a case study on the RISD Museum site, using publically-available metadata and interviews with staff to explore ways that metadata could be optimized to make objects of Latinx or Latin American provenance more accessible. (Note: I selected the RISD Museum due to proximity and relative access, since I am taking a class there this semester).

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Calling Something a Dataset: Visualizing the Crystal Palace

Since December, I’ve been working with Steven Lubar for a presentation at Bard College last week. The event focused on the 1853 New York Crystal Palace. Lubar has introduced his presentation over at Medium. (Parts one through three are up at the moment – the next installment will complement this piece.)

Most of my time with this project has been cleaning, wrangling, and interrogating the dataset. (Side note: does anyone remember that poem about literary analysis being like torture? It sometimes feels that way with datasets too.) As we’ve discussed in class, cleaning data is a never-ending process – the data we started with wasn’t raw, but carefully curated OCR from a Cornell Library database. In wrangling the data into a CSV and under headings specific to our needs, we shifted the catalog as a book into a catalog as a database, bringing up multiple questions of historical and technical interest.

Working through the data as a whole and as individual objects, Lubar and I worked to figure out what kind of questions could be explored in the catalog. What was this exhibition? What was interesting about it? What kind of information was collected for the catalog?

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Info Through Illustrator

About Me:
My name is Eric and I am currently pursuing a master’s degree in American Studies at Brown. I also work full-time at Brown in Athletic Communications.

Curation Project Idea:
For my curation project, I wanted to learn a new visualization application and use that design tool to present a small set of data in a more dynamic way. I regularly use Photoshop and InDesign with my everyday job, so I turned to Adobe Illustrator in order to make a more data-driven infographic, using college hockey players in the National Hockey League as the subject. The goals of the project were: 1) learn a new visualization application with some sort of graph component and 2) stick to the curation theme of the project by managing a larger set of data.

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Fake News: A Video Exploration

About Me:

My name is CJ. I am a senior at Brown University studying American Studies and Modern Culture & Media and pursuing a career in video journalism. More info on me and examples of my work may be found at https://www.cjrisman.com.

The Idea:

I went into this project hoping to play with the notion of “Fake News” and how the term had grown into a political tool utilized by the new administration. I was especially interested in the idea that Trump calling something “fake news” while being broadcasted on news networks became itself a form of actually fake news. Alone, this double layer can be read as a form of distortion, and I was curious how I could use video to manipulate those moments further.

The Form:

My project was in part inspired by a VICE News video of 21 things that go “bing” according to Trump:

 

I found this piece compelling as an archive of the unique language being employed by Trump as well as a type of distortion that brought together disparate moments in one political career. The various lower thirds that accompanied each clip also produced a secondary layer of narrative and distortion that one could choose to read into or not.

I set out to follow the form with the term “fake news.”

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Voyant, Read This For Me

Curation Project Concept:

This semester, I’m taking a graduate seminar called Reading Remains. We’re reading essays, most of them published in the last ten years, that discuss the practice of reading – how we should read, what stance we should take in relation to a text, how much or little we should engage emotionally, whether we should be suspicious of a text and seek to expose its hidden meaning, or whether we should read generously and assume that a text is openly presenting its meaning on the surface.

One of the challenges the course presents is keeping track of the terms. Different authors use different terms to describe slightly different reading styles: suspicious reading, symptomatic reading, surface reading, reparative reading, thin-description reading, thick-description reading, paranoid reading, to name a few.

So, I decided to upload the essays into Voyant in order to help me discern patterns in how the authors were using these terms. (Here are three examples of the eight I used in total).

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It’s Britney, Bitch: Commentary on Distortion in Spears’s Use of Social Media

THE IDEA

As someone who at times seems to only exist on Twitter, I’m fascinated by the ways in which people use the platform to craft their own narratives. But I also love the off-the-beaten-path accounts – bots, parodies, and what I like to call commentary.

I spend a lot of time looking at this third type, namely through examples of @WillMcAvoyACN and @SwiftOnSecurity. I stand by my point that these are not parody accounts. In the latter, as much as they’re prenteding to be Taylor Swift, they’re not going through the daily motions or documenting on her activities. Instead, it’s almost like she was dropped in an alternative dimension – same music, same image, but just talking about tech security instead of herself. Taylor’s persona, which was much more present at the beginning of the account than it is now, served as a gimmick to interest people in the content. McAvoy is a bit different – it’s a fictional persona – but it still prioritizes content over defined character. In his interactions with fans as well as real-life reporters, McAvoy serves as a commentary of journalism by playing through the persona as well as commenting on its usage to hold people accountable for the things they say.

So, as I wrote on Slack:

Inspired by the Twitter account SwiftOnSecurity, I’m looking to build a Twitter profile that engages/raises awareness around a keyword, but doing so through a persona that challenges or distorts the messages being presented. How does this observational view of a topic – one that a persona may have a reasonable stake in – change the ways in which we understand the topic? And in what ways do the tools available on Twitter – GIFs, photos, hashtags, polls, locations – distort or shape the ways in which we understand the project? Thinking around questions from Chun and Parise, why do we invest in this information even when the likelihood of the person behind it false? In what ways does this allow us to imagine the persona and the issues complexly? Can we explore these complexities in a project like this? In what ways do we see a persona like SwiftOnSecurity highlighting or hiding the relationship? Ideally, I’ll be looking at both my construction and reception by an audience (depending on what kind of audience is generated) to talk about how distortion functions in this environment.

 

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Seeing Gentrification

San Francisco is gentrifying. The Anti-Eviction Mapping Project’s message could be communicated in so many words. Instead, the project uses a chaotic, disparate collection of images, links, data, audio recordings, maps, and written narratives to digitally visualize San Francisco’s gentrification. The project’s approach to data visualization is one that leads with distortion. As a user, I was disoriented immediately upon entering the site. I didn’t know where to click first, where to look first, or what the site wanted me to do or understand. That first impression planted a seed of antagonism that remained with me for the duration of my interaction with the project.

But I trudged on, clicking more or less randomly through the maps, the reports, the narratives, the statistics, the visuals. Slowly I got the picture. Tech companies are moving to San Francisco and driving up real-estate prices, and lower-income, long-time San Francisco residents are being evicted. The eviction part I got right away. But the narrative that tech companies are precipitating these evictions took me longer to understand. I had to click through dozens of links before I found the text stating explicitly that certain companies, people, laws, and organizations were to blame for gentrification.

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