Why Digital Humanities? 12 Reasons for Media Historians

Why Digital Humanities? 12 Reasons for Media Historians

As media historian Bob Nicholson points out in his article “The Digital Turn,” while the downfalls of the digital turn in the humanities have been mapped out, affirmed, and reasserted, the “advantages of digitalization have been treated as too obvious to require explanation” (61). In this respect, it is important to draw attention to the possible strengths of a digital humanities approach. Here are twelve reasons for media historians to consider digital methods.

1. The Big Picture

There is a limit to the amount of individual texts, objects, sounds, and moving and still images that a researcher is able to read, watch, listen, and examine. Digital tools and methods have the capacity to account for a great mass of cultural artifacts, quantifying it into data, visualizing it on a grand scale, and allowing the researcher to identify patterns in the data that otherwise would be lost.

2. A Shift from Close to Distant Reading

Correspondingly, looking at a bigger picture and considering a complex mass of objects, texts, sounds, and images, some of which might be ephemeral, creates a shift in the scale of research from close to distant reading. In Graphs, Maps, Trees, Franco Moretti questions what happens when (literary) historians alter their gaze, comprehending distance not as a barrier, but as a way to reveal new forms of knowledge and understanding. Thus, for Moretti, distant reading, the use of quantitative methods to help identify patterns and elucidate interconnections across multiple texts, is the antithesis of close reading (1).

3. Production of New Forms of Knowledge

Digital tools create new sets of quantitative data, generally unfamiliar to many humanities scholars, to be interpreted, mapped, and visualized. Working with data sets has the potential to produce and represent new forms of knowledge, including new historical critiques, assessments, and narratives.

4. Visualization: Graphs, Maps, Trees

Digital tools produce visualizations to map and comprehend large amounts of data across space and time. In “Digital Visualization as a Scholarly Activity,” Martyn Jessop notes how digital visualization can be administered to any data and used in all areas of the humanities (291). For Jessop, the introduction of space through data visualization permits researchers to investigate patterns and interconnections not visible in written language (284). Furthermore, Nicholson asserts: “Whilst it is important to recognize the limitations of such an approach – it does not, after all, reveal the meaning of the texts it counts – it provides a useful way to visualize broad cultural trends and identify areas for closer inspection” (69).

5. Accentuation of Circulation through Data Visualization

Importantly, visualizing such forms of data can reveal the dynamic movement of cultural artifacts through time and space, illuminating new connections between particular objects of study (i.e., trade, fan, and academic discourses) and elucidating larger trends that may easily be overlooked.

6. Visual Comparisons

Creating visualizations of multiple data sets allows for comparisons. For example, as demonstrated by Eric Hoyt in the “Welcome to Project Arclight” video, a researcher can undertake a comparative analysis using visualizations that graph the differences in the career arcs of two actors.

7. New Questions Raised

Digital tools and approaches not only assist in answering our research questions but also lead to the exploration of unchartered territory, the drawing of previously unseen connections, and the formation of innovative questions that may not otherwise arise within a traditional media history methodology.

8. A Challenge to the Primacy of Text

A digital humanities approach encourages humanities researchers to both situate our objects of study as data and to contemplate the implications and potential problematics of such action. Doing so poses a challenge to the very primacy of text and textual analysis.

9. Collaboration

Since data can be interpreted in multiple ways and in various contexts, it can help foster collaboration among researchers. Moretti emphasizes its collaborative value, as data “are ideally independent from any individual researcher, and can thus be shared by others, and combined in more than one way” (5).

10. Communication and Accessibility

In his article “The Digital Inhumanities?” Scott Selisker argues that the biggest impact of digital humanities lies in “changing the ways scholars communicate their work to the public” (n. pag.). Digital humanities projects often maintain dedication to open source. Therefore, the digitization of archival materials, which are often kept hidden away under lock and key, has a huge impact on accessibility for scholars and the general public alike. Furthermore, projects like the Betham Project are eliciting help from the public through crowd-sourced transcription (see Cohen).

11. Comprehensive not Representative Sample

By focusing on general structures and patterns and moving away from individual texts, digital tools and methods can encourage a more comprehensive rather than representative sample, allowing for a shift away from canonical texts and received dominant histories.

12. Acceptance of the Unknown

Digital methodologies and distant reading uncover “the limits of what we can know about culture in the digital age” (Selisker n. pag.). Hoyt astutely points out that as a researcher this involves acknowledging that not everything can be digitized, accepting that materials will and do get lost, embracing various other complications that arise in algorithmic research, and reflecting upon the implications of this within the research process.

In examining the reasons why a digital approach may be useful to media historians it becomes apparent that digital tools not only have the capability to alter the ways in which media historians study media history, but they also “have the potential to transform the content, scope, methodologies, and audience of humanistic inquiry” (Burdick, Drucker, Lunenfeld, Presner, and Schnapp 3). The work of the Roy Rosenzweig Center for History and New Media (RRCHNM) provides strong evidence for the benefits of using digital tools in the study of media history, reflecting many of the reasons listed here. Over the last twenty years RRCHNM researchers have developed new digital software and methods (e.g., Serendip-o-matic, Omeka) to “democratize history” (n. pag.). In this respect, RRCHNM reinforces the value of digital tools and methods and

their capacity to help “incorporate multiple voices, reach diverse audiences, and encourage popular participation in presenting and preserving the past” (n. pag.). While the RRCHNM is one example of how digital forms open up media history methods and approaches, a variety of possibilities lie ahead and await our engagement.


Works Cited

About.” Roy Rosenzweig Center for History and New Media. 2014. 14 Nov. 2014.

Burdick, Anne, Johanna Drucker, Peter Lunenfeld, Todd Presner, and Jeffrey Schnapp. Digital_Humanities. Cambridge, Mass: MIT Press, 2012.

Cohen, Patricia. “Scholars Recruit Public for Project.” New York Times. 27 Dec. 2010. Web.

Hoyt, Eric. “Welcome to Project Arclight.” Online video clip. Vimeo, 13 May 2013. 17 Nov. 2014.

Jessop, Martyn. “Digital Visualization as a Scholarly Activity.” Literary and Linguistic Computing 23.3 (2008): 281-293.

Moretti, Franco. Graphs, Maps, Trees: Abstract Models for Literary History. New York: Verso, 2005.

Nicholson, Bob. “The Digital Turn.” Media History 19.1 (2013): 59-73.

Selisker, Scott. “The Digital Inhumanities?” “Two Rebuttals to ‘Literature is not Data: Against Digital Humanities.’” LA Review of Books. 5 Nov. 2012. Web.

How to Topic Model a Fan Magazine

At the 2014 Film & History Conference, Kit Hughes, Derek Long, Tony Tran, and I had the opportunity to lead an hour-long workshop titled “Historical Illuminations via Digital Tools: The Media History Digital Library, Project Arclight, and a Golden Age for Film History Research.”

Halfway through the workshop, we split off into small groups focused on particular methods of digital research. Derek talked about Lantern and search. Kit and Tony talked about Arclight’s new data mining method of Scaled Entity Search. I led a small group that wanted to learn more about topic modeling.

Due to our limited time, I had to generate the topic models on my own laptop and move quickly through the demo. However, many of the workshop participants expressed interest in experimenting more with topic modeling on their own. This post is written for those participants — and anyone else out there on the web — who wants to try topic modeling magazines from the Media History Digital Library.

A few suggestions up front — both conceptual and technical:

First, the conceptual suggestions: take a look at some of the excellent Digital Humanities scholarship that explains the process of topic modeling, discusses its value, and addresses strengths and weaknesses. I highly recommend two books by Matthew L. Jockers — Macroanalysis: Digital Methods and Literary History (2013) and Text Analysis with R for Students of Literature (2013). I also recommend reading Ben Schmidt’s critique of topic modeling, “Words Alone: Dismantling Topic Models in the Humanities” (2012).

To give us a working definition of topic modeling — and in the interest of everyone’s time, including my own — I’ll recycle the description of  topic modeling I provided in my 2014 Film History article, “Lenses for Lantern”:

The algorithm that powers topic modeling—latent Dirichlet allocation (LDA)—is extremely complicated, and lengthy articles have been written to explain it. For the sake of brevity, we can turn to Jockers, who offers the best two-sentence explanation I have been able to find about how the process works: “This algorithm, LDA, derives word clusters using a generative statistical process that begins by assuming that each document in a collection of documents is constructed from a mix of some set of possible topics. The model then assigns high probabilities to words and sets of words that tend to co-occur in multiple contexts across the corpus.” Essentially, you tell your computer to analyze a group of documents, you tell it how many topics you think are present in the documents, and you provide the computer with a “stop list” of words that you want it to ignore. (At the very least, a stop list should include common articles, such as a, and, the, etc.) The computer then returns word clusters that, hopefully, you can interpret as representing topics.

Everyone on board?

If so, let’s move on to the more technical suggestions/instructions. Before you can run your first topic modeling, you’ll need to:

  1. Download and install R and R Studio to your computer.
  2. Download the file workspace.zip, unzip it, and move it into a new folder on your computer. This is where you will do your modeling work. Do not try to save the workspace folder and perform your modeling work from your Desktop.
  3. Develop some comfort and confidence working with R from the command line (not 100% necessary, but it sure helps when you get your first error message and you are confused about how to proceed).
  4. Select a magazine volume from the Media History Digital Library that you want to topic model. Two sample text files are included in the workspace folder: Modern Screen (1937) and New Movie Magazine (1931). The R script will look for the em>Modern Screen file, though you can substitute a different file. You’ll want to download the TXT file of the magazine, which you can do by clicking “IA Page,” then “All Files: HTTPS”, then saving the file that ends “_djvu.txt”.

Ok, so now how to generate the models…

  1. Open R Studio and go to “Misc” > “Change Working Directory.” Set your working directory as the unzipped “workspace” folder.
  2. Open the file “topic_model_r_script.txt” and read it over. Focus especially on the comments — those lines that begin with a #.
  3. Make a decision about whether you want to use the “modernscreen_1937.txt” file (which corresponds to this magazine volume), or whether you want to select a different text file. Again, I’m biased in favor of the online material that’s part of the Media History Digital Library, but this process should work for most other text files too. You just need to modify this line of code to change what you are analyzing:    “text <- scan(“modernscreen_1937.txt”, what = “character”, sep = “\n”) #enter desired file here. we’re using the 1937 volume of the fan magazine.
  4. Make a decision about whether you want to add or remove any words in the “stop_list.txt” file. Your topic models will ignore any words on that list. Conjunctions, articles, and common first names are generally included in the list. For analyzing magazines, I also found that adding months to the stop list.
  5. Now copy & paste the code into R Studio and press enter. Give it a whirl. If it works, keep experimenting by setting the K variable (number of topics) at different numbers and by visualizing different topics as word clouds (which you control by changing “i <- 5” to “1 <- 3” or some other number).

What did your topic models tell you about the magazine?

In the case of Modern Screen, we generated some topics that perfectly fit our expectations — like this topic focused on dresses and fashion:

Modern Screen -- Fashion Topic Model

But we also found some topics that we weren’t expecting — like this one that featured the words “yeast” and “laxatives” prominently.

Modern Screen -- Yeast and Laxatives

We guessed that this topic had to do with the re-occurrences of particular word clusters in the advertisements of Modern Screen. I checked the magazine, and, sure enough, our assumption proved correct. But the nature of some of these ads defied our 21st century expectations. The yeast that so prominently rose (pardon the pun!) derived from a series of advertisements from the Ironized Yeast Company of Atlanta, Georgia. In ads such as those featured at the bottom this post, the Ironized Yeast Co. promised female readers that they would look more attractive if they took yeast tablets and gained weight. These ads suggest that while the idealized body type has changed over the last 80 years, the practice of advertisers exploiting our insecurities about how our bodies appear has not changed.

This example also shows how topic modeling is not simply about “distant reading.” What I’ve found is that topic modeling is most valuable when paired with close reading and browsing.

When I initially read through Modern Screen, I noticed the articles about celebrities, but not the Ironized Yeast Company’s advertisements. When my computer read Modern Screen, it brought the word “yeast” to my attention. And when I went back to read the occurrences of that word, it left me with a new series of questions about 1930s culture.

Topic modeling is one part of an iterative process that generates new questions, knowledge, and ideas.

Ironized Yeast Co. 1

Ironized Yeast Co. 1




Tyler Morgenstern

Tyler Morgenstern is a second-year Master’s student in Media Studies at Concordia University. Concerned with how the material, discursive, and affective conditions of (settler) colonialism and white supremacy shape experiences of intimacy in the contemporary moment, his thesis research (supported by the Social Sciences and Humanities Research Council) explores themes of embodiment, place, and race in recent works of media and screen art by racialized and Indigenous artists working Canada. Situating these works in the context of a post-9/11 Canada marked by dramatic shifts in the organization of citizenship, immigration, national security, and surveillance policy, he explores how particular aesthetic practices, by recasting the ways in which viewers experience space, throw into relief the material specificity of the contemporary Canadian state’s “atmospheric bordering regime,” disclosing and contesting its racialized, colonial, and gendered disciplinary valences. Morgenstern is also a member of the Feminist Media Studio at Concordia University, and, along with several scholars and artists from throughout the Americas, is currently in the early phases of a collaborative writing project focused on the aesthetic, political, and methodological possibilities bound up in the notion of trespass. As a member of the Arclight team, Morgenstern will be assisting in the digitization of selected archival materials and supporting the coordination of the Arclight symposium.