Category Archives: Social Network Analysis

Ekphrasis as an LDA Network in NodeXL

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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
Canto I by Dante Alighieri
The Hunter in the Snow by William Carlos Williams
Tiepolo’s Hound by Derek Wallcot
St. Eustace by Derek Mahon
Three for the Mona Lisa by John Stone
My Last Duchess by Robert Browning

Table 2: Ekphrastic Dataset 15 Topic Model, Topic 3 Highlighted

 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.

Table 3: Ekphrastic Dataset 15 Topic Model, Topic 4 Highlighted

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.

THATCampVA Tweet Visualization

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A NodeXL visualization of #THATCampVA tweets and people mentioned in them

The tweeting and the tweeted: THATCampVA in 140 character sprints

When you spend most of your time using a tool in a way in which it was not intended, sometimes it’s satisfying to try to use it for what it was meant to do. This network visualization of Twitter mentions from the past weekend’s THATCampVA proved useful to me for just that purpose. There’s no real argument here besides… this is kind of pretty. I used NodeXL, which is social network analysis (SNA) software, to do the calculations and visualization of the network. NodeXL allowed me to access the Twitter search API and pull in all tweets since April 19th that include the #THATCampVA hashtag. I used the Harel-Koren Fast Multiscale algorithm to create the visualization. Those included in the visualization are people who tweeted about someone else and those who were referenced within tweets. The “edges” or lines between pictures (also known as vertices) represent the direction of the relationship. In other words, arrows originate at the image of the twitterer who wrote the tweet and are pointing to the person tweeted about or to.

 

Curating a Network of Wood and Would

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In my current research, I argue that Elizabeth Bishop’s poem “The Monument” represents a more democratic attitude toward aesthetic objects than what we see from her contemporaries Robert Lowell and John Berryman.  Eschewing the “tutelary” relationship between poet as teacher and reader as student, Bishop offsets her own position of power as the artist-creator by including the voice of a resistant, reluctant onlooker whose interrogations about what it is they are supposed to be looking at position the reader as the monument’s curator, one who must select between descriptions, views, depth, and purposes for the monument.  The monument’s physical presence is brought into being collaboratively between the two speakers who create it’s shape and it’s potential and the reader who must parse and prioritize the verbal network of the poem.  Furthermore, Bishop, who started writing “The Monument” in her Key West notebooks (see Barbara Page) after just having read Wallace Stevens’ Owl’s Clover, enters into public discourse about the relevance of public monuments (and by extension art) in a social, political, and economic climate in which people are suffering, nations are warring, and the realities of daily life seem to negate the place and purpose of art.  Sounds familiar, no?

Bishop responds by arguing that monuments (and painting, and poetry, and sculpture) are significant because they are sites of “commemoration”–an interesting word choice because the word “commemorate” requires communal activity.  Unlike her friend Robert Lowell who uses the bronze relief by August Saint-Gaudens in Boston Commons to establish historical connection and significance for himself as an artist, Bishop imagines the monument as evolving to purpose rather deriving from it.

Visualizing the poem as a network demonstrates how the speakers’ relationship to one another builds out of their discursive description of the monument.  In the networks below, each speaker is related to the monument through questions and description (recorded as the statements they make in the poem).  I have characterized those statements as grounding the monument in physical space, with tangible attributes, insisting on its materiality [repesented by blue lines] or on the other hand imagining the monument’s potential or possibility through questions, equivalences (is it this or that statements) and statements that intimate a metaphysical presence for the monument [in orange].

 

The networks are formed by matching each speaker up with with each statement made in the poem.  As a result, this is a “bimodal” network: one which takes actors and text and studies the relationship between them.  The relationship, represented by a line is further characterized as advancing the monument’s status as “wood” (a physical object belonging to the material, and therefore “real” world) and the word’s homophone “would” (a representation of possibility, potential, and by association the “imaginary” life of the mind).

The reader as curator, then, must choose among the wood and the would–between the multiply rendered descriptions of the monument’s physical presence and its imagined potential, which is a new beginning itself, the shape of which could be poetry, painting, statue or monument, depending on choices the reader makes.

Monument as Descriptive Network

Preparing texts for network visualization

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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.

Networks and Network Analysis for the Humanities: Reunion Conference

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On October 20-22, the UCLA Institute for Pure & Applied Mathematics will host the “Networks and Network Analysis for the Humanities: Reunion Conference.”  Participants were 2010 NEH summer institute participants and they will present their projects during this 2 day event.  Coincidentally, Robert Nelson, this week’s MITH digital dialogues guest speaker will be one of the presenters at the IPAM event.

Looking at this list of projects and preparing to discuss network analysis at MSA13 this weekend prompted me to make a list of known projects using (social) network analysis to pursue questions in the humanities.  I’ve created a page where I attempt such a thing, and will post it on this site soon.