Close Reading Re-visited

Monica Bulger, Elizabeth Lagresa, Jessica C. Murphy, Jeff Scheible

 

In this project, we digitally analyze student texts, ballads, translations, and a theoretical piece to discover new connections and patterns not immediately visible in their traditional structures. Using this method, we zoom out and view the structures and forms of the texts, rather than focusing on sentence-level rhetorical moves. We then blend traditional methods of literary interpretations with textual visualizations to better understand the connections that underlie our chosen texts.

 

Distance reading: how do visualizations provide understanding of texts?

By zooming away from sentences, words, and ideas, did our new interpretations clarify or distort our understanding of trends within the texts?

 

Our tools

We experimented with several tools and realized that in many cases, we were using online textual analysis tools against their intended purpose. For example, in Jessica's examination of word frequency in the ballad "A Young Man's Opinion," she began with a tag cloud, but later realized that she wanted to examine the relationship between word use, not necessarily view which words occurred most often, so she shifted to using a text tree. Similarly, Monica used a plagiarism detection tool to explore how students used source materials in academic assignments. Rather than "policing" students with this tool, she used it to better understand students' composing processes.

 

Experimenting with various tools forced us to refine our questions. For example, after testing many tools, Jeff settled on using Microsoft Word to visualize how Derrida employed parentheses in his arguments.

 

Conclusions

Ultimately, we found that digital and traditional methods of close reading inform and complement each other. Below, we more thoroughly explain our individual methods and discoveries.

 

Pairwise re-mix, Monica Bulger

 

Tool: Pairwise

 

 

 

 

Findings: Digital textual analysis potentially provides the opportunity to examine large amounts of texts in scant time (Moretti, 2007). While designed to perform a punitive function of identifying plagiarism, detection tools such as Pairwise, CopyCatch, and Turnitin compare multiple texts against each other, identify similarities between the texts, and generate a visual representation of these text matches, making them ideal candidates for digital text analysis. Click here to view research report.

 

 

Translating the Text, Elizabeth Lagresa

 

Tool: Babylon

 

 

 

Findings:see Translation Project Details

 

Word Frequency Visualization, Jessica C. Murphy

 

Tool: Many Eyes

 

 

Monica Bulger's Expert Essay	Monica Bulger's Novice Essay

 

 

 

Findings: Tag clouds are used often on blogs as an at-a-glance view of the content and a navigational tool. Based on word frequency, the tag cloud returns a quick view of a user-supplied text. The word tree, a cousin of the tag cloud, renders words by frequency and also by context because it associates words with all of the other words with which they occur. I uploaded a selection of each member's text into IBM's Many Eyes's word tree visualization in order to use it against its intended purpose. We were looking specifically for elongated readings--that is, a sense of how words function in a text as a whole rather than the quick glance of the typical word frequency. This technique proved to be very fruitful with each of our texts. For example, Monica's student essay and expert essay differed in the way that they used the word "I," which reveals something about the way the writer uses sources (type in "i" in the trees below to see this example). My text, "A Young Man's Opinion," is a list of all of the things that make a woman good, but about which he claims he does not care. The young man says he wants only a woman who is good to him. The visualization reveals his list of the attributes of a good woman as a list, thus undermining his claim of indifference (see my research report for that visualization; if you type in "care," you will see what he really does care about). This visualization, whether purposefully or not, also renders punctuation. Seeing the punctuation in Jeff's and Monica's texts was very revealing. Elizabeth's texts are translations of the same play soliloquy, and the choices that the translators make are made very clear by the visualization.

 

 

 

Nothing but Punctuation, Jeff Scheible

 

Tool: Microsoft Word

 

 

 

 

 

 

 

 

 

 

 

 

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Team Notes Section

 

Notes from first meeting:

 

This project proposes to use similar methodologies on very different texts. Because our texts vary (and the outcomes are likely to as well), we hope to gain insight into the approaches we use.

 

Each member of the team will be using different sources as the basis for her or his textual analysis using variety of tools from our Toy Chest. Some of the tools we discussed during our first project meeting are:

 

TAPoR: recipes to explore and identify themes, TADA is one of them

Crawdad: analyzes word frequencies with importance, need 30-day free trial

Many Eyes: visualize word frequencies (need data set)

 

Rama has also suggested aiSee and Graphviz as possibilities.

 

What do we hope to gain individually?

• Elizabeth plans to utilize various text-analysis tools from the toy chest to visualize word frequencies along with their importance, identify usage patterns and themes in Calderon's La vida es sueño, and ultimately contrast them to those found in English-language versions. Elizabeth's Project Details.
• Jeff is interested in visualizing the use of parentheses in Derrida's essay, "Signature Event Context." This often-cited theoretical essay about deconstruction and writing contains many parentheses, and his use of them seems to be very related to the ideas he discusses in his essay. By visualizing the parentheticals in a variety of ways, some interesting observations about his theory and his writing might emerge. It also somehow seems appropriate to do a sort of reconstruction of deconstruction.
• Monica is analyzing how people use source materials in their texts, which includes parenthetical citations, etc., numbers, tag phrases (texts will be a series of student writings collected as part of her dissertation research).
• Jessica would like to find word groupings in ballads about feminine virtue from the seventeenth century (texts are ten broadside ballads from the Pepys collection). Some preliminary "tag clouds" of the ballads are available here.

 

What do we hope to gain as a team?

• Discover the implications of using these different methods on such a variety of different texts
• Through experimentation, uncover the possibilities in our data sets
• Recognize the possibility for cross-disciplinary use of these methods (or the opposite--that some tools work better for some kinds of texts and others for different kinds)

 

Our first task is to experiment with the available tools and see which ones might reveal something about our materials that we had not seen yet.

 

February 19, 2008 Project Meeting Notes (Jessica)

 

Jeff is thinking about maybe taking all of the words out to see what it looks like?, Elizabeth is interested in the limitations of the tools, translators, perspectives on the text by human translators, etc.

 

 

• Important to keep in mind: What are our research questions? What are we trying to find out? Some things still need to be done manually.
• maybe we are each interested in reverse engineering stuff, deconstructing a deconstruction essay, translation is sort of trying to mess it up
• deforming or dismantling texts and tools
• using the tools against their original intention
• limit to stay in-depth versus going too broad
• understanding of our role as co-creators of literature becomes really clear when dealing with translations

 

What we plan to do:

• take apart the text
• take apart the tools
• appropriating tools for one purpose for another?

 

 

We will each take a tool and report back to each other:

• Elizabeth: translator
• Jeff: Microsoft Word
• Monica: Pairwise
• Jessica: Tag Cloud

 

Then we will use snippets of our texts in each of the tools to see the results.

 

Texts: Monica has 154, Jessica has 10 but is thinking of using only one, Elizabeth has one play and translations, Jeff has one essay (and two translations)

 

 

 

February 26, 2008

Some notes on Microsoft Word as a tool, by Jeff

 

As Jessica says above, we've each chosen a "tool" and decided to report back to each other so that we might generate some ideas about useful things that we can do with each of our texts with the various tools we're considering. We also decided to pay particular attention to ways of trying to put our tools to subversive uses--uses that they may not necessarily be designed for.

 

It occurred to me that one might take something as familiar as Microsoft Word as a tool (according to our friend, Wikipedia, after all, it was first released in 1983 as "Multi-Tool Word") and think about the ways in which one could use it to "deform" a text, rather than write one, which is what we are accustomed to doing with the software. Just think of its "toolbox," and of all the everyday deformative metaphors the application uses, which are mostly grouped in its "edit" menu: "cut," "copy," "paste," "undo," "redo," etc. Thinking about all of these in many ways really emphasizes how the process of writing is in many ways already a process of deformance.

 

Assuming all of us can transfer our texts to word documents--be it manually, by typing ourselves, or by copying and pasting it, or transferring file formats, here are a few particularly deformative practices one could use MW for:

 

1. Find and replace. With this editing option, one could choose any textual element of the text, large or small, and replace it across the text with any other textual element of one's choice. So one could replace all uses of the word "virgin" with the word "whore." Or one could replace all uses of parentheses with exclamation marks. The tool can also find and replace font styles--so one could pick a repeated phrase or letter in a text that is particularly important for analysis and choose to make them all bold or italicized.

 

2. If one makes changes with the "track changes" option featured in the "tools" menu, different users can send versions of their documents to each other, and changes each one makes will be viewable in the text and in its margins in color-coded text balloons. As a tool that already does interesting visual things with the text, the track changes feature in this context might lead to very interesting ways of re-looking at our texts as we deform them, to actually visualize the deformance itself. Alternatively, in the "insert" menu, there is also an option to leave "comments," where one doesn't have to edit the text, but one can still highlight parts of the text and write notes about it in balloons in the margin.

 

3. One can change font colors. Simply by using this tool to make certain parts of our text white in color can serve to erase its visiblity in the text but retain the text's spatiality, in a way that simply deleting would not do, as that would delete the space of the text one might not want to see. So, for example, I can make white all of Derrida's essay that is not in parentheses, so that only the parentheticals remain, and thereby retain a sense of their spatial relationships and organization within the original text.

 

4. Draw tables. This might be a way to visually mark-up and space out our texts.

 

5. Margins, line spacing (what would it look like to set line spacing of a text to ".5," so that it gets all scrunched up?), the possibilities for text-altering seem endless.

 

 

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During our Workshop on February 28, 2008, each team member worked a little bit with the other team members' texts. Jessica created word trees using Many Eyes for Jeff's, Monica's, and Elizabeth's base texts. A sample of those are below. Elizabeth translated Jessica's ballad ("A Young Man's Opinion") into Spanish and then back into English using Babylon.

 

MacCarthy's Translation of "Life is a Dream" in a word tree

 

Alan's tips about the final project presentation for March 13th:

Turn Project Page into more formal presentation of the project

presentations=20 minutes per time (our team is about 5 min each)

highlights, not necessarily a thorough guide through the project