I always welcome guest blog posts, and this month I’m delighted to feature an inspiring post by Jon Heggestad about his exciting work in Digital Humanities (DH). As one of the leading students of DH at SBU, Jon’s contributions to the university’s DH community have been significant, and he has also been a pioneer of public writing among graduate students. I’ve always loved hearing about his projects, so I couldn’t have been happier when he suggested contributing a guest post to this site. Please read on to learn about Jon’s fascinating work on mpreg (male pregnancy) fan fiction.
Jon Heggestad is a fourth-year PhD candidate in the English department at Stony Brook University. With interests at the intersection of literary analysis, queer and feminist theories, and the digital humanities, Jon’s dissertation traces out a queer lineage of the pregnant man as a literary trope.
A Study in Data Visualizations: Describing Mpreg in Fan Writing
In Digital_Humanities, an introductory textbook to the field penned by many of the leading voices in DH, distance reading is referred to as “almost not reading at all.” The authors of this volume refer to it instead as engaging “the abilities of natural language processing to extract the gist of a whole mass of texts and summarize them for a human reader in ways that allow researchers to detect large-scale trends, patterns, and relationships that are not discernable from a single text or detailed analysis.”1
This post expands and challenges this definition by delving into one offshoot of distance reading: data visualization. While the use of charts, graphs, and pictures has a history as old as humanity itself, recent works by scholars like Matthew L. Jockers and Franco Moretti have popularized computational methods of data visualization within the humanities.
As much of my own work is conducted around texts found (or even born) online, I’ve taken to computational ways of reading to help me sift through the immense bodies of work I often encounter. In the study below, I illustrate how data visualizations have aided my work and suggest that, rather than viewing this method as “almost not reading at all,” we instead view data visualizations as merely different, albeit less traditional forms of reading, and that these differences might lead us to new epistemological understandings as well.
“Mpreg,” short for “male pregnancy” might not be a term that the general public is yet familiar with, despite its steady rise in popularity over the past two decades as a fanfiction trope.2 That being said, its increase in popularity has garnered it more notoriety as well, with Flourish Klink and Elizabeth Minkel, the creators of the popular podcast Fansplaining, pointing to it as one of the “most widely reviled” tropes in fanfic today.3
And certainly mpreg is divisive among fans. While some warmly take to the idea of Sherlock Holmes rubbing Watson’s pregnant belly or Draco Malfoy and Harry Potter arguing over which crib to buy, other readers find these narratives “twisted and perverse.”4
In my own interactions with those new to the genre, however, I’ve found that the most common response is simply one of curiosity. What is mpreg? What does it entail? Yet, answering these questions isn’t the easy task that it might at first appear to be.
Archive of Our Own (AO3), a site hosting millions of fan-made stories, returns over 43,000 texts when “mpreg” is entered in the search field.5 One of the most useful questions that distance reading responds to is how to deal with such a large corpus.
One possible solution is offered through data visualization, which can provide—at a glance—a level of insight that might not be achieved even after weeks of reading these texts. That being said, I’ve still elected to draw some constraints around the scope of my inquiry, analyzing only the 1,400+ mpreg works found in the MTV series Teen Wolf fandom.
Using a text scraper created by Jingyi Li and Sarah Sterman (current graduate students), I gathered the metadata from the Teen Wolf mpreg fics in a spreadsheet, isolating the tags writers had used to identify their works.6 After cleaning this data, I then used Tableau (a data visualization software) to organize and display the results. The bar graph in fig. 1 depicts the 50 most common tags used by mpreg writers in the Teen Wolf fan community.7
“Alpha/Beta/Omega Dynamics” is the most frequently used tag, and it comes as no surprise for fans who know that mpreg is a subgenre within the larger Omegaverse genre of fanfic. Frequent occurrences of tags like “Mates” and “Derek and Stiles are Mates” indicate a common Omegaverse practice noted by Milena Popova, which relies on a “life-long psychic bond with a partner.”8 In Teen Wolf, this OTP (One True Pairing) is most common between the characters Derek and Stiles. The visualization above allows viewers to confirm at a glance many of these important elements in the Teen Wolf fics.
Continuing this analysis, the popularity of tags like “knotting” and “fluff” illustrates the coexistence of what’s often regarded as opposing poles in mpreg practices: erotic writing vs. domestic narratives. In order to better understand these coexistent tropes, I changed the bar graph above to a stacked bar graph (fig. 2), displaying the category divisions that make up each tag count. “Categories” on AO3 refer to the intended audiences of fanfics.9 What I found is that tagging practices in this fandom transgress the boundaries that I initially expected: “fluff” is a tag frequently used in fics labeled for “Mature” and “Explicit” audiences, for example, while “smut” and “knotting” are used multiple times in fics intended for “Teens and Up.”
So what do these visualizations tell us about methodology? In her “Introduction: Data Visualization and the Humanities,” Elyse Graham identifies two main ways that data visualizations are used: explication and discovery.10 While she goes on to say that this earlier use is more prevalent, I’ve shown how both methods can occur simultaneously in the visualizations above. Not only was I able to quickly indicate for those new to mpreg what the main themes of the genre are, I also came to the realization that the binaristic division I’d been imposing on mpreg was, in fact, blurred by the writers of these fics.
Lastly, Graham notes that one benefit of using data visualizations “is the practice of inquiring into its own conditions, even if doing so sometimes makes the discussion cautionary rather than celebratory” (9). While agreeing with her call to use data visualization methods for reflective purposes, I can’t help but be excited regarding the new questions that emerge with new distance reading methods.
Certainly, data visualizations look very little like our traditional ways of reading. But as N. Katherine Hayles points out in How We Think, there are more ways to read than one. And learning how to read in these new ways can be an enriching practice for all.
1 Burdick, Anne et al. Digital_Humanities. MIT Press, 2016, p. 39. 2 While I identify mpreg as a trope here; it’s also frequently referred to as both a genre and a fetish. 3 Klink, Flourish, and Elizabeth Minkel. “Five Tropes Fanfic Readers Love (And One They Hate).” Fansplaining, 27 Oct. 2016, https://www.fansplaining.com/articles/five-tropes-fanfic-readers-love-and-one-they-hate. 4 Shrayber, Mark. “What Exactly Is Mpreg? A Male Pregnancy Enthusiast Explains.” Jezebel, 3 Nov. 2014, https://jezebel.com/what-exactly-is-mpreg-a-male-pregnancy-enthusiast-expl-1651553874. 5 These numbers reflect a search conducted on October 14, 2019. 6 “Tagging” in fan communities refers to the practice of adding metadata to and “imposing order” on fanfics so that they can be more readily found by the readers searching for these terms. McCulloch, Gretchen. “Fans Are Better Than Tech at Organizing Information Online.” Wired, June 2019. https://www.wired.com/story/archive-of-our-own-fans-better-than-tech-organizing-information/. 7 This list has removed “mpreg,” which appeared far more often than any of the other tags and thus skewed the rest of the graph. 8 Popova, Milena. “‘Dogfuck Rapeworld’: Omegaverse Fanfiction as a Critical Tool in Analyzing the Impact of Social Power Structures on Intimate Relationships and Sexual Consent.” Porn Studies, vol. 5, no. 2, Apr. 2018, pp. 175–91, p. 7. 9 AO3 allows fic writers to categorize their works by their intended audiences: general, teen and up, mature, or explicit. Those fics that aren’t categorized as one of these four options are automatically labelled “not rated.” 10 Graham, Elyse. “Introduction: Data Visualisation and the Humanities.” English Studies, vol. 98, no. 5, 2017, pp. 449-458, p. 2.