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
This post was a DHNow Editor’s Choice selection on May 1, 2012.
The research I am doing presently uses visualizations to show latent patterns that may be detected in a set of poems using computational tools, such as topic modeling. In particular, I’m looking at poetry that takes visual art as its subject, a genre called ekphrasis, in an attempt to distinguish the types of language poets tend to invoke when creating a verbal art that responds to a visual one. Studying words’ relationships to images and then creating more images to represent those patterns calls to mind a longstanding contest between modes of representation—which one represents information “better”? Since my research is dedicated to revealing the potential for collaborative and kindred relationships between modes of representation historically seen in competition with one another, using images to further demonstrate patterns of language might be seen as counter-productive. Why use images to make literary arguments? Do images tell us something “new” that words cannot?
Without answering that question, I’d like instead to present an instance of when using images (visualizations of data) to “see” language led to an improved understanding of the kinds of questions we might ask and the types of answers we might want to look for that wouldn’t have been possible had we not seen them differently—through graphical array.
Currently, I’m using a tool called MALLET to create a model of the possible “topics” found in a set of 276 ekphrastic poems. There are already several excellent explanations of what topic modeling is and how it works (many thanks to Matt Jockers, Ted Underwood, and Scott Weingart who posted these explanations with humanists in mind), so I’m not going to spend time explaining what the tool does here; however, I will say that working with a set of 276 poems is atypical. Topic modeling was designed to work on millions of words, and 276 poems doesn’t even come close; however, part of the project has been to determine a threshold at which we can get meaningful results from a small dataset. So, this particular experiment is playing with the lower thresholds of the tool’s usefulness.
When you run a topic model (train-topics) in MALLET, you tell the program how many topics to create, and when the model runs, it can output a variety of results. As part of the tinkering process, I’ve been working with the number of topics to have MALLET use in order to generate the model, and was just about to despair that the real tests I wanted to run wouldn’t be possible at 276 poems. Perhaps it was just too few poems to find recognizable patterns. For each topic assignment, MALLET assigns an ID number to the topic and “topic keys” as keywords for that topic. Usually, when the topic model is working, the results are “readable” because they represent similar language. MALLET would not call a topic “Sea,” for example, but might instead provide the following keywords:
The researcher would look at those terms and think, “Oh, clearly that’s a nautical/sea/sailing” topic, and dub it as such. My results, however, on 15 topics over 276 poems were not readable in the same way. For example, topic 3 included the following topic keys:
3 0.04026 with self portrait him god how made shape give thing centuries image more world dread he lands down back protest shaped dream upon will rulers lords slave gazes hoe future
I don’t blame you if you don’t see the pattern there. I didn’t. Except, well, knowing some of the poems in the set pretty well, I know that it put together “Landscape with the Fall of Icarus” by W.C. Williams with “The Poem of Jacobus Sadoletus on the Statue of Laocoon” with “The New Colossus” with “The Man with the Hoe Written after Seeing the Painting by Millet.” I could see that we had lots of kinds of gods represented, farming, and statues, but that’s only because I knew the poems. Without topic modeling, I might put this category together as a “masters” grouping, but it’s not likely. Rather than look for connections, I was focused on the fact that the topic keys didn’t make a strong case for their being placed together, and other categories seemed similarly opaque. However, just to be sure that I could, in fact, visualize results of future tests, I went ahead and imported the topic associations by file. In other words, MALLET can also produce a file that lists each topic (0-14 in this case) with each file name in the dataset and a percentage. The percentage represents the degree to which the topic is represented inside each file. I imported the MALLET output of topics and files associated with them into Google Fusion Tables and created a dynamic bar graph that collects file-ids along the vertical axis and along the horizontal axis can be found the degree that the given topic (in this case topic 3) is present in the file. As I clicked through each topic’s graph, I figured I was seeing results that demonstrated MALLET’s confusion, since the dataset was so small. But then I saw this: [Below should be a Google Visualization. You may need to “refresh” your browser page to see it. If you still cannot see it, a static version of the file is visible here.]
If the graph’s visualization is working, when you pass your mouse over the lines in the bar graph, the ones that are higher than 0.4, then the file-id number (a random number assigned during the course of preparing the data) appears. Each of these files begin with the same prefix: GS. In my dataset, that means that the files with the highest representation of topic 3 in them can all be found in John Hollander’s collection The Gazer’s Spirit. This anthology is considered to be one of the most authoritative and diverse—beginning with classical ekphrasis all the way up to and including poems from the 1980s and 1990s. I had expected, given the disparity in time periods, that the poems from this collection would be the most difficult to group together because the diction of the poems changes dramatically from the beginning of the volume to the end. In other words, I would have expected the poems to blend with the other ekphrastic poems throughout the dataset more in terms of their similar diction than by anything else. MALLET has no way of knowing that these files are included in the same anthology. All of the bibliographical information about the poems has been stripped from the text being tested. There has to be something else. What something else might be requires another layer of interpretation. I will need to return to the topic model to see if a similar pattern is present when I use other numbers of topics—or if I include some non-ekphrastic poems to the set being tested—but seeing the affinity in language between the poems included in The Gazer’s Spirit in contrast to other ekphrastic poems proved useful. Now, I’m not inclined to throw the whole test away, but instead to perform more tests to see if this pattern emerges again in other circumstances. I’m not at square one. I’m at a square 2 that I didn’t expect.
The visualization in the end didn’t produce “new knowledge.” It isn’t hard to imagine that an editor would choose poems that construct a particular argument about what “best” represents a particular genre of poetry; however, if these poems did truly represent the diversity of ekphrastic verse, wouldn’t we see other poems also highly associated with a “Gazer’s Spirit topic”? What makes these poems stand out so clearly from others of their kind? Might their similarity mark a reason for why critics of the 90s and 2000s define the tropes, canons, and traditions of ekphrasis in a particular vein? I’m now returning to the test and to the texts to see what answers might exist there that I and others have missed as close readers. Could we, for instance, run an analysis that determines how closely other kinds of ekphrasis are associated with Gazer’s Spirit’s definition of ekphrasis? Is it possible that poetry by male poets is more frequently associated with that strain of ekphrastic discourse than poetry by female poets?
This particular visualization doesn’t make an “argument” in the way humanists are accustomed to making them. It doesn’t necessarily produce anything wholly “new” that couldn’t have been discovered some other way; however, it did help this researcher get past a particular kind of blindness and helped me to see alternatives—to consider what has been missed along the way—and there is, and will be, something new in that.
lmrhody says: