An article I wrote for ProfHacker at the Chronicle of Higher Education about using NodeXL for social network analysis came out yesterday. This is the first article in a two-part series introducing academics to how one might begin evaluating his or her own online, scholarly social network. In the first entry, I gesture to the ways in which social media network analysis in general and NodeXL in particular presents scholars with rich opportunities to learn more about the shape their participation in social media takes. The second post, to be published Thursday, March 28, focuses more specifically on how NodeXL helps us make sense of conference backchannel conversation, taking as my example this year’s Twitter feed from the MLA 2013 convention.
In the weeks ahead, I plan to write short tutorials for scholars on how to use NodeXL to better understand their use of social media for scholarly communication. My hope is that more academics take the time to evaluate their scholarly networks and that the activity sparks more conversation about how social media participation could or should be considered as a contribution to scholarship and to consider how social media can be used to expand one’s scholarly audience.
Today, ProfHacker published the second of two posts I’ve written about using social media network analysis and visualization software called NodeXL, titled “NodeXL: Learning from Visualizations of Social Media Networks.” In this post, I talk about what we can learn from importing, calculating the metrics of, and visualizing Twitter conversations that include a hashtag (#) related to a conference. In the example I provide, I talk about the Modern Languages Association convention that happened last January. I’m looking forward to writing a few more posts about how to get started with NodeXL in the coming months.
In an earlier post, I mention the value of visualizations as a means for exploring topic modeling data. That particular example used a small model of 276 poems labeled “ekphrastic” out of a much larger collection. At that point, I was still struggling with how to read the data, which felt overwhelming. How could I organize the relationships between topics and documents in such a way as to see salient connections produced by the model? The intermediate solution was to break the model down into groups of 3 topics and create bar graphs charting the likelihood that each document contained language from each topic. That solution worked in the short-term, because it helped me to discover the fact that one topic was found highly likely within a particular volume of ekphrastic verse: John Hollander’s The Gazer’s Spirit.
Still, what I wanted was an impressionistic overview of the documents’ association with all of the topics. The first 40 or so attempts at this process were a dismal failure. Partly because it was a learning process and partly because the results frequently resembled the much maligned “hairball,” what I produced was completely incomprehensible. However, August 20th to 24th I attended the NSF, Social Media Research Foundation, and Grand funded Summer Social Webshop on Technology-Mediated Social Participation. There, I met Marc Smith, who began developing NodeXL, a social media network analysis tool built to work with Microsoft Excel, while he worked for Microsoft Research. Marc, who now leads the Social Media Research Foundation and Connected Action generously took time to demonstrate how to import my topic modeling data into NodeXL so that I could generate graphs that are more elegant and streamlined than any I’ve been able to produce to this point. The results aren’t just beautiful: they’re useful.
So, what are those results? They include unimodal and bimodal network graphs that visualize connections between documents with other documents, topics with other topics, and documents with topics created with an LDA model in MALLET. Using NodeXL’s algorithms, I am able to cluster groups with stronger ties in grid areas, assign them unique colors, and demonstrate the degree of probability the model calculates as a connection between nodes (either documents or topics depending on the graph). The real power of NodeXL, though, is that in the future I can make my data public through the NodeXL gallery, and you can download my network graph and play with it yourself. The data isn’t quite there yet, but that’s what’s coming.
In the meantime, I’ll offer the following image of a network graph that I had hoped to produce with my earlier post about The Gazer’s Spirit. Though the topic label is small, Topic 3 can be seen in the top left hand corner of the network diagram. The width and color of the edges in the diagram (meaning the width of the lines) is determined by the model’s estimation of how much of each topic is in each poem. If the lines are thicker and lighter, it means that the model estimates that a large portion of the poem draws its language from the corresponding topic. Similarly, the thinner and darker a line is the lower the probability that the poem includes language from the corresponding topic.
Table 1: Ekphrastic Dataset – 276 poems and 15 topics
Topic 3 (in the top, left-hand corner) is primarily comprised of connections to poems from The Gazer’s Spirit and is affiliated by language that reflects a kind of courtship, including archaic references (thy, thee, thou) and the language of love (er, beauty, grace, eyes, heaven, divine, hand, love). This makes sense in the context of existing knowledge about Hollander’s volume. The collection reads very much like a tribute to painting and the visual arts by poetry, and the language of desire is prevalent throughout. Moreover, both W.J.T. Mitchell and James A.W. Heffernan, two prominent theorists in the ekphrastic tradition, insist that the language of love and desire is a strong, if not dominant, discourse across all of ekphrasis based on a canon of poems mostly included in The Gazer’s Spirit. One might assume, then, that there would be strong connections between a topic comprised of the language of courtship, love, and desire and most of the poems in the collection; however, only a few of the poems with a statistically significant portion of its language from Topic 3 are not also in The Gazer’s Spirit: “The Picture of Little T.C. in a Prospect of Flowers,” “The Art of Poetry [excerpt],” “Ozymandius,” “Canto I,” and “My Last Duchess.” Of those poems, none are by female poets.
Poems with highest proportion of Topic 3
The Temeraire (Supposed to Have Been Suggested to an Englishman of the Old Order by the Flight of the Monitor and Merrimac) by Herman Melville
To my Worthy Friend Mr. Peter Lilly: on that Excellent Picture of His majesty, and the Duke of York, drawne by him at Hampton-Court by Sir Richard Lovelace
From The Testament of Beauty, Book III by Robert Bridges
For Spring By Sandro Botticelli (In the Academia of Florence) by Dante Gabriel Rosetti
To the Statue on the Capitol: Looking Eastward at Dawn by John James Piatt
The Poem of Jacobus Sadoletus on the Statue of Laocoon by Jacobus Sadoleto
To the Fragment of a Statue of Hercules, Commonly Called the Torso by Samuel Rogers
The Last of England by Ford Maddox Brown
On the Group of the Three Angels Before the Tent of Abraham, by Rafaelle, in the Vatican by Washington Allston
Death’s Valley To accompany a picture; by request. “The Valley of the Shadow of Death,” from the painting by George Inness by Walt Whitman
Elegiac Stanzas Suggested by a Picture of Peele Castle, in a Storm, Painted by Sir George Beaumont by William Wordsworth
On the Medusa of Leonardo da Vinci in the Florentine Gallery by Percy B. Shelley
The Mind of the Frontispiece to a Book by Ben Jonson
Venus de Milo by Charles-Rene Marie Leconte de Lisle
The City of Dreadful Night by James Thomson
Sonnet by Pietro Aretino
For “Our Lady of the Rocks” By Leonardo da Vinci by Dante Gabriel Rosetti
Mona Lisa by Edith Wharton
Ode on a Grecian Urn by John Keats
The National Painting by Joseph Rodman Drake
The “Moses” of Michael Angelo by Robert Browning
Hiram Powers’ Greek Slave by Elizabeth Barrett Browning
From Childe Harold’s Pilgrimage, canto 4 by George Byron Gordon
The Picture of Little T. C. in a Prospect of Flowers by Andrew Marvell
Before the Mirror (Verses written under a Picture)Inscribed to J. A. Whistler by Algernon Charles Swinburne
For Venetian Pastoral By Giorgone (In the Louvre) by Dante Gabriel Rosetti
The Art of Poetry [excerpt] by Nicolas Boileau-Despreaux
Ozymandias by Percy B. Shelley
The Iliad, Book XVIII, [The Shield of Achilles] by Homer
The only remaining topic which includes the word love fairly high in the key word distribution is Topic 4, which includes the following terms: portrait, monument, foreman, felt, woman, monuments, box, press, bacall, detail, young, thick, crimson, instrument, hotel, compartment, picked, cornell, Europe, lovers. As you can see from the network diagram below, none of the topics with high probabilities of containing Topic 3 are included in the Topic 4 distribution.
Equally interesting, poems with the highest proportion of Topic 4 are also authored by female poets. Certainly, more poems by men include significant proportions of Topic 4 than poems by women that include significant portions of Topic three; however, there are striking and salient points to be made about the contrasting networks:
Poems with highest proportion of Topic 4
“Utopia Parkway” after Joseph Cornell’s Penny Arcade Portrait of Lauren Bacall, 1945 – 46 by Linda Hull
Canvas and Mirror by Evie Shockley
Portrait of Madame Monet on Her Deathbed by Mary Rose O’Reilley
Internal Monument by G. C. Waldrup
The Uses of Distortion by Caroline Crumpacker
Joseph Cornell, with Box by Michael Dumanis
Drawing Wildflowers by Jorie Graham
The Eye Like a Strange Balloon Mounts Toward Infinity by Mary Jo Bang
Visiting the Wise Men in Cologne by J.P. White
Rhyme by Robert Pinksy
The Street by Stephen Dobyns
The Portrait by Stanley Kunitz
“Picture of a 23-Year-Old Painted by His Friend of the Same Age, an Amateur” by C.P. Cavafy
Portrait in Georgia by Jean Toomer
For the Poem Paterson [1. Detail] William Carlos Williams
The Dance by William Carlos Williams
Late Self-Portrait by Rembrandt by Jane Hirshfield
Sea Life in St. Mark’s Square by Mary O’Donnell
Washington’s Monument, February, 1885 by Walt Whitman
Still Life by Jorie Graham
Still Life by Tony Hoagland
The Family Photograph by Vona Groarke
The Corn Harvest by William Carlos Williams
Portrait of a Lady by T. S. Eliot
Portrait d’une Femme by Ezra Pound
This impressionistic overview of the ekphrastic dataset prompted through the exploration of a network graph of the relationships between topics and poems is a first step. Enough, perhaps, to formulate a new hypothesis about the difference between “love” and “lovers” in ekphrastic poetry, or to lend further support to the growing sense that there is a much broader range of kinds of attraction and kinship—a range inclusive of both competitive and kindred discourses—than previous theorizations of the genre have taken into account. The network visualization goes further than to suggest that there are two very different discourses regarding love and affection in ekphrastic verse, but even suggests possible poems to consider reading closely to see what those differences might be and if they are worth pursuing further. Through the use of networked relationships between topics and documents, we begin with lists of poems in which the discourse of affinity, affection, and desire—as courtship or as partnership—can be further explored through close readings.
Meeting Edward Tufte’s claim that evidence should be both beautiful and useful, the NodeXL network diagrams of LDA data are a step toward developing methods of evaluating and exploring models of figurative language that do not necessarily fit the same criteria for models of non-figurative texts.
Scott B. Weingart says: Great post as always! I am curious how you decided your cutoff for keeping the edges of “poems with a statistically significant portion of its language from” particular topics, and which edges you decided to throw out. Also, just a quick suggestion, making the stronger edges lighter rather than darker was visually confusing, maybe reverse that or change the color scheme somehow?Reply
lmrhody says:Scott, You ask a great question. In this case, there are several “trimmings” built into the process. In the first layer, the script that I use for converting the model into an Excel workbook with individual spreadsheets (one for document distributions, one for word distributions, one for word probability distributions, one for topic to topic symmetrized K-L Divergence, one for document to document symmetrized K-L divergence, and one for document to topic probabilities, among a few others) allows me to establish a threshold that the topic to document likely proportion must meet in order to be included in the spreadsheet. In this instance, that threshold was .1. In the portion of the post you’re referring to, I list all the documents with an estimated topic proportion greater than 10%. In this case, that’s 35 documents out of 276. That’s not to say that documents with lower proportions might not also render useful results, but I feel fairly confident that my top 35 are sound. In fact, the list is cut and paste almost directly from my spreadsheet, so the poems are listed in order of their probability score, from highest (.8) to lowest (.1). Same is true for Topic 4, meaning that the further down the list you go, the lower the proportion of the poem is estimated to come from that topic.Regarding the graph’s design, I agree. The difficulty is that if you switch the colors (dark blue=strong, light blue=weak) then you completely overwhelm the weaker ties, and they can no longer be seen. Though the weaker ties are… well… weaker, they aren’t insignificant. As I mentioned, proportions lower than .1 were trimmed out. Perhaps it would make more sense to use just one visual cue (either thickness of edge or color saturation)–however, that also limits the ability to read the graph. For someone who is color blind, the use of color alone would inhibit understanding the degree of relatedness. Similarly, having a single color with variations in thicknesses of the edge makes a much more difficult graph to read, especially if when you print it out in black and white you still want to be able to distinguish between ties that overlay with one another. Admittedly, yes, it does create a sense of sensory confusion, but I went with what I felt were the lesser of two evils in this case. HOWEVER, in my document to document graph where I’m able to do really interesting things with clustering algorithms (and I’m really excited about how well this has been working), the darker=stronger works better because the position of the documents is more forcefully determined by symmetric K-L divergence.At some point in the not too distant future, I plan to post some of these graphs on the NodeXL gallery site. If you download the free NodeXL plug-in/template and then download my network graphs, you can play around with it, too. I’m eager to see what other people can do with my graphs.Reply
Scott B. Weingart says:Thanks! This adds a lot to the original post; I definitely look forward to seeing where this goes. I asked about your network because I’ve recently been doing similar work, and Ted Underwood apparently has as well. We should all chat about it sometime.Reply
lmrhody says:Will you be coming to the NEH-sponsored Topic Modeling Workshop in November? I’m going to be there. I don’t know if Ted Underwood will be there, but I hope so. Perhaps, we will have a chance to get together and talk–and maybe even find something to collaborate on–there. Thank you for your questions and your comments, which have been really helpful as I’m working on revisions.Reply
Scott B. Weingart says:I’ll be there! I don’t think Ted will, but we can definitely chat then, would love to discuss this project more.Reply
When I presented at MSA 13 earlier this month, I was unsatisfied with my methods for creating network visualizations of texts. I knew that preprocessing automatically would not work yet, since I have yet to identify precisely how I want to designate nodes across larger bodies of poems. What I’ve been looking for is a way to mark texts up descriptively, using some form of markup language (XML, TEI), that would be uniform enough to render data that could be meaningfully displayed, and then to find a visualization software package with an algorithm that would “work” the way I wanted it to. The problem, of course, is that when you’re a rogue DH scholar out in the world borrowing tools and using whatever tends to fall your way, then you’re not going to be sure about how each tool works (unless you have a CS or social science degree that includes learning about network algorithms, which I do not have), and this is going to detract from the validity of how and what you say about your object of study. On the flip side, tools and text analysis software are becoming more widely available, and so doing what I’ve done, which is to say Googled “discourse network tool” and finding Philip Leifield’s “Discourse Network Analyzer” is actually possible. What is remarkable about how DNA, a GUI text processing software, works is that it is designed as an interpretive tool to mark texts up in XML so that they can be displayed using free network visualizing software such as Visone, Ucinet, or Netdraw. The designed purpose of Leifield’s DNA software is to collect articles on a topic area and to use those articles to create network visualizations of agreement and disagreement between individuals and groups. For example, the sample dataset used for a tutorial on the software comes from someone at the University of Maryland named Dana R. Fischer, (I have no idea who she is… but I’m definitely going to look her up!) who marked up articles, testimony, and other texts about climate change. Essentially, she could input each text into the DNA software and create a basic XML document with very minimal encoding (document type, author, dates, title) and then use DMA to select portions of text that create a “statement” about climate change. By tagging the speaker, the organization the speaker is affiliated with, and the content type –a restricted list of terms created by the user to describe the topic being discussed—as well as whether or not the speaker agreed or disagreed with the topic) she could create networks of statements made about climate change that also included the individuals involved in the climate change debate and their organizations. Such a visualization helps us to understand how much any one group (say, the Senate and the EPA) agree with one another, to identify the issues on which they agree and disagree, and to also understand affiliations (which speakers are affiliated with which climate change debates).
This isn’t *exactly* what I had in mind, but it’s really darn close. The power of this particular piece of software is that I can be in charge of what constitutes an article (a poem), what constitutes a speaker (the poetic speaker, the author, the third person omniscient… all of them), and the “content” to be described. Granted the “organization” classification is less helpful to me, but in the instance of “The Venus Hottentot (1825)” I could differentiate between speakers from the first section of the poem from the second using this feature. Using the software this way does not begin to utilize it’s real power, which is to read topics and speakers over large corpuses of texts in similar ways. For now, I’m looking at one poem; however, I could see in the future were I to take this poem and situate it in a larger public discourse about black female subjectivity, I could. I could import, for example, Sander Gilman’s article “Black Bodies, White Bodies: Toward an Iconography of Female Sexuality in Late Nineteenth-Century Art, Medicine, and Literature,” which we know Elizabeth Alexander read before writing the poem. We could also bring in articles by Sadiah Quershi on “Displaying Sara Baartman” or Terri Francis’s “I and I: Elizabeth Alexander’s Collective First-Person Voice, the Witness and the Lure of Amnesia,” or chapters from Deborah Willis’s Hottentot Venus 2010 and demonstrate how Alexander’s poem participates in a larger act of social recovery.
There are, as with any tool, limitations, though. So far, the only way to create the visualizations is using the speakers, organizations, and categories with directional lines indicating agreement or disagreement. I have not found a way of creating networks of “statements.” In other words, I have not found a way to pull a category and then visualize the network of statements about that category and how they relate to each speaker; however, I have only begun the process of creating visualizations. Another complication is that I have only found ways to make a statement associated with one category. I’m fairly certain I can find a work around for that, but for the moment, that’s not worked out; however, I will say that having to choose between regular category designations (ones of my own creation) did make me very attuned to my assumptions about the text. That process helped me to realize how my visualizations of these networks will always be limited and remind me that I need to make those limitations transparent when I write about what the visualization actually visualizes.
In the meantime, even though I am not teaching right now, I’m really excited about what this kind of software could mean for my students. In the English 101 courses at the University of Maryland, students write three linked assignment papers on a self-selected research topics. These are position papers, where the student must make purposeful arguments for what he or she believes in and respond to the discourse of the field in which their selected debate is ongoing. We generally assign an annotated bibliography as the first part of that linked assignment as a way of getting students to read the work and to then explain who agrees with each other on particular points and who disagrees. The hard part of this assignment is that each entry is generally 2 paragraphs long and includes only 8-10 sources, and getting the students to actually compare arguments, identifying points of agreement and disagreement is difficult. However, if the assignment were to use the Discourse Network Analyzer to import each article and then go through each article tagging “statements,” “speakers,” “organizations,” and “categories” (for example, are the speakers arguing that a particular action should be taken or that one event causes another…) as well as “agreement” or “disagreement” with that statement, they might begin to see how their readings create a network of ideas and by understanding who agrees and what they agree upon, the student might be better able to situate him or herself within the discourse of that issue. It’s an intriguing idea to me, and at some point when I’m teaching again, I think I’m going to make use of this technology.
lmrhody says: