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Learn about the completed DiSC project of Dr. Deepika Bahri, Postcolonial Studies @ Emory. 

It is with great pleasure that I announce the existence of the new home for Professor Deepika Bahri’s Postcolonia Studies @ Emory Website.  Over the past year, many members of the DiSC team, graduate students, library staff and Professor Bahri worked together to create a more sustainable site for Bahri’s multi decade archive of important people and issues in postcolonial studies.

Dr. Bahri’s website predates Wikipedia and anticipated the types of information web users would be looking for in relation to postcolonial studies. The site has been reorganized to provide easy navigation to postcolonial Authors & Artists, Critics & Theorists, and Terms & Issues. On each page you can learn about a person or issue through the lens of one of her students. The student generated content of the site represents an early digital pedagogy project, as each page was a course assignment for a student in one of her classes. From 1991 to the present, the website has amassed some 160+ pages of postcolonial studies material. Some Wikipedia pages even site the website as a source. With over 20,000 hits a day, Professor Bahri’s original website is an important resource for many on postcolonial studies.

2011-2012 graduate fellow Franky Abbott initiated an audit of the site where each page of the website was copy edited and evaluated. As project manager, I worked with graduate students, to link relevant content to each other through the creation of a tagging system that allows you to see certain content nodes throughout the site. Tim Bryson and Sandra Still provided important theoretical support for the construction of the tags.

The majority of the work involved cleaning up the content of the website. Undergraduate DiSC student employee Victoria Tao, was initially tasked with doing simple editing of each page, checking for spelling errors and inherited word merging that was the result of the script used to pull the content from the existing website. Tao’s expertise in html was later utilized as we modified the wordpress theme to best suit Dr. Bahri’s needs. Once this was completed, graduate students with specialization in postcolonial studies edited the pieces for content errors, correcting places, names and facts in each article.

Can digital tools always make our research more innovative—or sometimes, do they just get in the way?

At DiSC and the Beck Center, we've been mulling over the question of whether the push to "go digital" with a project is always a good thing. Accordingly, we were curious to see what we'd find if we used a bunch of digital text analysis programs, and then we simply read the texts. Would the digital programs offer new insights and save us time? Or would they clutter up an otherwise straighforward textual analysis?

We tested several free, open source tools by using them to analyze  57 sermons given after Lincoln's assassination. The sermons, which are digitized and housed on the Beck Center's website, present the perfect opportunity to experiment with digital analysis.

Did the digital tools pass the test? We arrived at a typical humanities answer: yes and no. You can find detailed results on the project site, which we're calling "Lincoln Logarithms: Finding Meaning in Sermons." The tools we employed— Voyant, Viewshare, PaperMachines, and MALLET—quickly offered us some potential research questions and highlighted places and subjects that we might look at more closely.

Perhaps most enticingly, the programs "read" the 57 sermons (comprised of 1672 pages and 481,575 words) in mere seconds. Reading them was more tedious; in fact, no one on our team made it through all of them. In this case, I think that actually reading the texts—even just a portion of them—was more valuable than looking at the digital tools' output.

Digital tools can help us hone in on what questions to ask. They are a way to help us arrive at questions and results, but they aren't results.

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THATCamp Feminisms South  is a free “unconference” for anyone working at the intersection of feminism, the humanities, and technology. It will be held at Emory University's Digital Scholarship Commons on March 15-16, 2013. People of all professions and skill levels are welcome.

THATCamp Feminisms will provide a space for participatory learning through discussion, skills-based workshops, and on-the-spot experimentation. We hope to inject critical conversations about gender and race into the digital humanities, which has been criticized as mostly white and male-dominated.

Because THATCamp is an “unconference,” our participants will determine the ground we cover. Depending on your interests, we may hold sessions on issues in digital scholarship, pedagogy, curation, software demos, or training sessions. In other words, you won’t present formal talks, papers, or panels. Instead, you’ll share your ideas for potential THATCamp sessions prior to the Camp. At the start of the event, we’ll collectively agree on which conversations will take place.

The brief application is available at http://feminismssouth2013.thatcamp.org/register/. Registration is open now, and the deadline is March 4, 2013.

Please email thatcampfeminismssouth@gmail.com with any questions, and don’t forget to follow us on Twitter (http://twitter.com/THTCmpFeminisms). We look forward to hearing from you!

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5:00pm usually signifies the end of the workday, but for dozens of Emory faculty, staff, and students, the early evening has become an opportunity to squeeze in some much-needed supplemental software training.

During the month of October, 40 people from across the university gathered in DiSC weekly for the first ever Lynda.com working group.

Lynda.com is a video learning site stocked with tutorials on hundreds of websites, software applications, and computing functions. Emory has maintained a paid subscription to Lynda.com for some time, but only a handful of people have become regular users.

For the inaugural goup, we chose to focus on Microsoft Excel, an application useful in areas ranging from humanities and social science research to human resources and administration. Though dozens of hours worth of Excel training are available on Lynda.com, the sessions focused on broadly applicable functions like sorting, filtering, pivot tables, and database management.

"I've known that Lynda is availabe since I started working at Emory," notes one attendee, "but I never got around to using it until I signed up for the scheduled sessions. It's easier to stick to a training schedule this way."

Emory is currently transisioning to a workstation-based subscription to Lynda.com. Beginning later this month, anyone with an Emory ID will be able to access the site through designated workstations in the Woodruff Library (in ECIT and the Research Commons) and at the Health Sciences Library.

We're currently accepting suggestions for our next training series. If you'd like focused training on a particular piece of software, let us know.

Topic Modeling output for a Zotero collection using Paper Machines

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Paper Machines, the add-on that integrates a range of text analysis tools into Zotero, has generated quite a buzz in the short period of time since its release. For those of us that store notes, citation information, PDFs, and article links in huge Zotero libraries, Paper Machines has the potential to be a game-changer in terms of how we visualize our research.

Because Paper Machines is so new, it's being updated with added functionality every few days. I'll provide step-by step documentation for how to use specific components of Paper Machines in Part II of this post. For now, I'll discuss whether or not Paper Machines might be a good fit for your research, the tools that it offers, and how it might help your work.

Paper Machines provides a broad range of text analysis tools, but it's not meant for everyone's research. You'll probably benefit most from Paper Machines if you:

1.  Already use Zotero to manage your sources. Paper Machines draws on a number of open source tools available elsewhere on the web. If you want to visualize your data but aren't already comfortable using Zotero, you might want to look elsewhere.
2. Have a relatively large or robust Zotero library. At the time of this posting, Paper Machines incorporates the full text of Web snapshots and OCR'd PDF files into its text analysis, as well as the title, place, date, and subcollection of a source. The option to include notes, tags, and links to live websites will be available shortly.
3.  Are collaborating on a Zotero library with a group. Paper Machines is very good at helping you figure out the contents of a collection. If you're working on a collection with multiple group members, it's a quick way to visualize what kinds of material your collaborators are adding.

What kinds of analysis tools does Paper Machines employ?

• A word cloud with the option to filter out commonly used words.
• Phase nets, which allow you to visualize relationships between common words in your text (for example, x and y; x is y)
• A Geoparser, which uses location information to produce beautiful visualizations of the places mentioned in your texts.
• DBpedia Annotation, which produces a visualization of what people, places, and things are mentioned in your texts.
• MALLET-based topic modeling, which generates visualizations based on commonly occurring topics in your texts. The author offers some additional information about information about Paper Machines' use of topic modeling here.

What can Paper Machines help you do?

• Assess the contents of a collection. Looking through the Paper Machines results is a helpful way to get to know the contents of a group library or to get reacquainted with a collection that you haven't used for a while.
• Identify gaps in your material. Reviewing the MALLET output for a specific collection in my Zotero library (canonical works in US history) I noticed a surge in books about women's labor history (which MALLET identified using the terms women, labor, work, and activism) during the 1980s. I also noticed a lack of items in my library about these topics since 2000.
• Compare collections. Analyzing two collections with Paper Machines makes similarities and differences evident. Using topic modeling, for example, I could see what subjects came up most frequently in the two collections and if they coincided. The word cloud function is the easiest way to identify concurrent terms and subjects at a glance.
• Find patterns in your collections. Using the "Phrase Net" function, I conducted an "x is y" analysis on one of my collections. I was surprised to see that "democracy is necessary" and "Cold War is necessary" were recurring phrases a number of sources.

The Geoparser links texts in a Zotero collection to the places that they mention.

Additional examples of Paper Machines visualizations are available on the developer's site. The add-on is available for Firefox and Zotero Standalone, and visualizations  can also be saved as html files. While the occasional error or puzzling result is inevitable early on, the creator of Paper Machines is constantly tweaking the interface in response to feedback from users.

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Beck Center Grad Fellow Sara Palmer tells us how MALLET works and what you can (and cannot) do with Topic Modeling

This past summer I have enjoyed working with a tool called MALLET (MAchine Learning for LanguagE Toolkit) that can generate a series of potential topics in a corpus of texts.  Created by Andrew McCallum at University of Massachussetts, it is an open source toolkit and that is fairly easy to download and install.  While the setup instructions on the website are minimal, Shawn Graham’s has written a helpful overview of how to get started.  

Topic modeling is based on the idea that within a set of related texts, certain words will occur near each other with statistically significant frequency. MALLET works on documents like its acronymic namesake by shattering texts into an array of words.  It then applies a statistical method called Latent Dirichlet Allocation to put related words into clusters. 

These related groups of words, or ‘word bags’ as they are often called, can be interpreted as making up a topic.  For example, the group of words ‘needle,’ ‘stitching,’ and ‘fabric’ could reasonably be labeled as the topic ‘sewing.’  Of course, scholars familiar with a corpus can readily name a list of topics but what if you’re working with a large set of texts and don’t have time to read all of them carefully?  MALLET allows you to quickly obtain a general idea of what topics may be present.  

Perhaps the most prominent example of topic modeling with MALLET is Cameron Blevins’s work on the diary of Martha Ballard, an American midwife who wrote daily entries for 27 years.  Blevins’s topic model identified key themes in the diary such as ‘death,’ ‘shopping’ and ‘gardening’ and graphed their prominence over the course of Ballard’s life.  These charts often corresponded with known biographical details.  For example, the topic ‘emotion’ peaked between 1803 and 1804 when her husband was imprisoned for debt and her son was indicted for fraud.   

The field of DH itself has also been put under the MALLET “macroscope.”  Elijah Meeks’s work visualizes the network of associated ideas in the self-definitions of digital humanists while Matt Jockers analyzes the work performed in the field by culling themes from the blog posts of 117 digital humanists on a single day (March 18, 2010).  The Maryland Institute of Technology in the Humanities (MITH) has an excellent overview of how topic modeling has been used in the humanities.  Other prominent topic modeling projects featured on the MITH blog include Travis Brown’s work on Austen and Byron and Jeff Drouin’s work on Proust. 

Interest in topic modeling has grown at DiSC since it hosted a talk by Robert Nelson from the University of Richmond this past January.  In his research on the American Civil War, Nelson ran MALLET on the issues of Richmond Daily Dispatch newspaper from 1860-1865.  He was able to graph and contextualize topics such as fugitive slave ads and military recruitment, the results of which are illustrated beautifully on the project’s website.  For each topic Nelson has a separate page displaying the graphs of its prevalence over time, the most prominent key words, as well as the articles, ranked by compositional percentage, that most clearly exemplify the topic.  

These two components, keywords and compositional percentage, can be respectively derived from the MALLET output files ‘topic-keys’ and ‘doc-topics.’  At the Beck Center we wanted to see how this output might be used for understanding the journal Southern Changes, published monthly by the Atlanta-based Southern Regional Council from 1979 through 2003.  Comprised of 110 issues containing 978 articles, it is considerably smaller than the data set Nelson worked with but still large enough to make it a good fit for MALLET’s method.  We hoped to identify key terms with which to catalogue the collection and also to generate topical groups of articles that could be featured on the website.  The results were encouraging as I found the themes suggested by the topic keyword lists to roughly correspond with the subjects provided by Allen Tullos, editor of Southern Changes from 1982 through 2003.  Tullos writes:

 “The articles in Southern Changes range across many subjects: racial justice and the freedom struggle, voting rights, educational opportunity, economic democracy, social equality and inclusion, women's rights, environmental justice, critical regional studies, regional-global issues, and popular culture.”

After discovering a ten-topic run of MALLET to be a bit broad, I opted for 20 topics.  In THIS TABLE I have labeled the topic keyword lists with my overall impressions in black.  The generally correspondent Tullos subjects—labeled in green—are included for most of the MALLET topics.  This is, of course, a very rough sketch but it does appear to suggest correlation between MALLET data and expert knowledge.

As a model built on sampling and probability, MALLET naturally does not generate the same exact output each time it is run.  The topics and keywords do vary but are generally similar from one run to the next.  Below is a table comparing the keywords from the ‘Voting Rights’ topic in the previous example to those produced in five additional runs.  The original run was based on 10,000 iterations but for the sake of time, I used 1,000 iterations on the additional trials.  VIEW THOSE RESULTS HERE

While the topic-keys outline the word composition of the topics, the doc-topics indicates the topic composition of each document.  It simply lists, in descending order, the ID number of each topic and the percentage of the document that it makes up.  CLICK HERE for a table of the documents in which ‘Voting Rights’ comprises over 40% of the composition.

From this one can get a clearer picture of what kind of content is picked up by MALLET for the ‘Voting Rights’ topic and see that the topic is most prevalent in the journal around the 1980 and 2000 election cycles. 

In sum, MALLET did what we wanted it to do: we obtained key terms and a general overview of topics present.  My own attempts to graph the topics over time did not illustrate trends with nearly as much clarity as I would have hoped.  However, my objective is not to prove that MALLET is some kind of magic bullet, ripping through a corpus to the heart of its intelligibility.  Rather, I found that the output, when interpreted cautiously, can offer an excellent starting point for more detailed content analysis.  

Even if data visualization isn't the primary goal of a project, adding an animated or interactive map can be an effective way to enrich a presentation, article, or lecture--and it doesn't have to take up huge swaths of time. As part of the Tweeting Occupy Wall Street project, I tested web-based mapping tools that would allow us to plot some of the 10 million tweets related to Occupy Wall Street.

Dozens of geographic data visualization tools, many of them open-source, are available on the web, but for this particular project I investigated tools that are 1) powerful enough to handle large data sets; 2) relatively easy to learn and share; and 3) free. Here's a rundown of the two tools that I found to be most effective in the Tweeting #OWS project: OpenHeatMap and Geocommons.

OpenHeatMap allows users to create static or animated heat maps (also called intensity or chloropleth maps) based on data uploaded through a Google Doc or Excel spreadsheet. Heatmaps plot values in a range of colors that indicate intensity, similarly to a meteorological radar map. One of the most user-friendly tools that I tested, all OpenHeatMap requires to generate a map is 1) location information, in the form of latitude and longitude or state/country abbreviations; 2) a column of values (used to plot intensity); and 3) if you want an animated heatmap, an optional column marked "time."

Customization options in OpenHeatMap allow creators to control features like color and size of the data and map. After customizing, a user can simply use an autogenerated code to embed the map into an website. Alternatively, Open HeatMap offers an option to host the map on your own site and fully customize it using the Heatmap API. My attempt to do so was unsuccessful, however, and and a number of sources suggest simply using the embed code to store and share a map.

More than a site for creating maps, Geocommons is a robust data analysis, management, and visualization platform. Like its name suggests, Geocommons embraces the open-source model and strongly encourages users to make their maps and data public (20 megabytes of private data storage is also available with a free membership). This means that, along with uploading data, users can access hundreds of data sets including census data, zip code and county maps, and much more.

It's possible to run analyses on data from within Geocommons, but I found it to be much faster to do the process in Excel or Google Docs first and upload the finished dataset that contained the values I wanted to plot. Geocommons makes it easy to aggregate data into non-map geographic visualizations, like this chart I make of the top states with Twitter activity related to Occupy Wall Street.

Geocommons serves up simple embed codes for its maps, as well as a customizable Javascript API geared toward site developers. Like OpenHeatMap, embedding maps is either straightforward and noncustomizable or tricky and flexible. I'd suggest using the standard embed code unless you have some experience with Javascript.