Indexes, graphs, maps, and trees: data visualization in the digital humanities

N.B.: This post will serve mostly as context for my interests in data visualization, mapping, and network analysis, for the purpose of the University of Alberta’s Digital Humanities course.

Folklorists often focus on narrative: Russian Structuralist Vladimir Propp famously claimed there are 31 functions of a fairy tale. He argues that “functions of characters serve as stable, constant elements in a tale, independent of how and by whom they are fulfilled. They constitute the fundamental components of a tale” (21). My research argues that the advent of relatively new media for narrative transmissions (television, internet, etc.) and their cultural consequences (franchises, fandoms, etc.) have radically affected the ways North American (and to some extent, European/Eurocentric) culture produces, consumes, and receives fairy tales. So analysing a folktale with Propp’s 31 functions may tell me part of the story, but not all of it.

Context/Once Upon A Time

To fully understand the stories fairy tales offer, though, it’s important to understand how fairy tales have been conceptualized in the past, and how I propose fairy tales should be considered in contemporary contexts. Antti Aarne (1910), Stith Thompson (1928, 1961), and later Hans-Jörg Uther (2004) adapted a much more detailed and complicated system to classify folklore than Propp’s 31 functions. The Aarne-Thompson-Uther index (ATU) organizes folklore by categorizing each tale with a number and cross-referencing it with a letter. It’s a bit long (so feel free to skip down at any point), but Uther explains the lettering system in “Classifying Tales: Remarks to Indexes and Systems of Ordering”:

     The letters:

  1. Mythological Motifs
  2. Animals
  3. Tabu
  4. Magic
  5. The Death
  6. Marvels
  7. Ogres
  8. Tests
  9. The Wise and the Foolish
  10. Deceptions
  11. Reversal of Fortune
  12. Ordaining the Future
  13. Chance and Fate
  14. Society
  15. Rewards and Punishments
  16. Captives and Fugitives
  17. Unnatural Cruelty
  18. Sex
  19. The Nature of Life
  20. Religion
  21. Traits of Character
  22. Humor
  23. Miscellaneous Groups of Motifs
    According to content, further subdivisions are made, for instance[:] group M is subdivided as follows:
    Ordaining the Future: Judgments and Decrees (Mot. M 0 – M 99)
    Vows and Oaths (M 100 – M 199)
    Bargains and Promises (M 200 – M 299)
    Prophecies (M 300 – M 399)
    Curses (M 400 – M 499)

Tormod Kinnes has helpfully and publicly uploaded the very long list detailing the ATU index’s numerical system:
The numbers:

Wild Animals 1-99
The Clever Fox (Other Animal) 1-69
Other Wild Animals 70-99
Wild Animals and Domestic Animals 100-149
Wild Animals and Humans 150-199
Domestic Animals 200-219
Other Animals and Objects 220-299
Supernatural Adversaries 300-399
Supernatural or Enchanted Wife (Husband) or Other Relative 400-459
Wife 400-424
Husband 425-449
Brother or Sister 450-459
Supernatural Tasks 460-499
Supernatural Helpers 500-559
Magic Objects 560-649
Supernatural Power or Knowledge 650-699
Other Tales of the Supernatural 700-749
God Rewards and Punishes 750-779
The Truth Comes to Light 780-799
Heaven 800-809
The Devil 810-826
Other Religious Tales 827-849
  The Man Marries the Princess 850-869
The Woman Marries the Prince 870-879
Proofs of FidelitY and Innocence 880-899
The Obstinate Wife Learns to Obey 900-909
Good Precepts 910-919
Clever Acts and Words 920-929
Tales of Fate 930-949
Robbers and Murderers 950-969
Other Realistic Tales 970-999
Labor Contract 1000-1029
Partnership between Man and Ogre 1030-1059
Contest between Man and Ogre 1060-1114
Man Kills (Injures) Ogre 1115-1144
Ogre Frightened by Man 1145-1154
Man Outwits the Devil 1155-1169
Souls Saved from the Devil 1170-1199
Stories about a Fool 1200-1349
Stories about Married Couples 1350-1439
The Foolish Wife and Her Husband 1380-1404
The Foolish Husband and His Wife 1405-1429
The Foolish Couple 1430-1439
Stories about a Woman 1440-1524
Looking for a Wife 1450-1474
Jokes about Old Maids 1475-1499
Other Stories about Women 1500-1524
Stories about a Man 1525-1724
The Clever Man 1525-1639
Lucky Accidents 1640-1674
The Stupid Man 1675-1724
Jokes about Clergymen and Religious Figures 1725-1849
The Clergyman is Tricked 1725-1774
Clergyman and Sexton 1775-1799
Other Jokes about Religious Figures 1800-1849
Anecdotes about Other Groups of People 1850-1874
Tall Tales 1875-1999
  Cumulative Tales 2000-2100
Chains Based on Numbers, Objects, Animals, or Names 2000-2020
Chains Involving Death 2021-2024
Chains Involving Eating 2025-2028
Chains Involving Other Events 2029-2075
Catch Tales 2200-2299
Other Formula Tales 2300-2399

If you’re looking for a public database of tales organized by ATU type, check out D.L. Ashliman’s Folktexts: A library of folktales, folklore, fairy tales, and mythology.

The problem with these tale-types, though, is that they don’t quite address the narrative complexities of mainstream folklore, like the fairy tales seen/told/disseminated in ABC’SOnce Upon A Time. So my dissertation is interested, among other things, in how ABC, which has been owned by Disney since 1996, has begun to incorporate various fairy tales previously unassociated with Disney’s World within its purview. Thus the Beast from Disney’s iconic Beauty and the Beast (1991) is Rumpelstiltskin as well as Belle’s lover (fig. 1). Or there’s Lana Parilla, whose role as Regina Mills, the mayor of Storybrooke (the show’s contemporary real-world setting), also means acting as the Evil Queen from Snow White (1937) (fig. 2) and Ursula from The Little Mermaid (1989) (fig. 3).

Belle (Emilie de Ravin) and Rumpelstiltskin (Robert Caryle), Once Upon A Time

Fig. 1 Belle (Emilie de Ravin) and Rumpelstiltskin (Robert Caryle), Once Upon A Time

Fig. 2 Regina Mills/Snow White's Evil Queen (Lana Parilla), Once Upon A Time

Fig. 2 Regina Mills/Snow White’s Evil Queen (Lana Parilla), Once Upon A Time

Fig. 3 Evil Queen/Ursula (Lana Parilla), Once Upon A Time

Fig. 3 Evil Queen/Ursula (Lana Parilla), Once Upon A Time

Now here I will be borrowing from work I’ve previously done during my M.A., but I promise it will only be a branching point for further research/discussion. Because I think my point comes across with some pretty simple data visualizations (fig. 4).

Fig. 4 My  very basic venn diagram

Fig. 4 My very basic venn diagram

This first visualization, for example, is pretty rudimentary (and aesthetically ugly) but it communicates the gist of my dissertation (embarrassingly enough): ABC’s narrative manipulations are conglomerating Western folklore. The next visualization is aesthetically more sophisticated (fig. 5), but conceptually, it doesn’t communicate too much more, although it does make explicit a few things (namely, that ABC operates as the intermediary between Disney’s fairy tale, Beauty and the Beast, and the more publicly/freely consumed narrative, “Rumpelstiltskin,” and also becomes the force that encompasses what was once a freely consumed narrative into a packaged Disney product ready for en-franchising).

FIg. 5 My second try at the network/venn diagram

Fig. 5 My second try at the network/venn diagram

In the following graphs (figs. 6-8), I tried to make clear the importance of a network-based analysis, totally indebted to Franco Moretti’s “Network Theory, Plot Analysis.”

Fig. 6

Fig. 6

Fig. 7

Fig. 7

Fig. 8

Fig. 8

What I think these graphs make clear are the overlap of corporate narratives with folkloric ones. Purnima Bose and Laura E. Lyons argue that

companies are always engaged in a kind of storytelling aimed at improving their public image and justifying their actions. Corporations and their CEOs are in the position of Scheherazade. As long as they have a story to tell that is at least captivating enough they can keep themselves alive for one more day. These stories play a role in suturing or resolving contradictions and in rationalizing seemingly arbitrary and brutal decisions. But there is increasing demand that these narratives be reliable and have some mimetic accuracy. Within the complex web of social, political, and economic relationships that constitute ‘the world of business,’ some stories are getting harder to sell. (3, emphasis mine)

Yet Disney’s stories are, in many ways, becoming easier to sell, with Netflix already beginning to offer exclusive streaming of new Disney releases as well as Disney classics from the Vault. Disney’s also negotiating legal conceptualizations of public narratives with the rise of corporate copyright legislation like Canada’s Copyright Modernization Act (2011), the U.S.A.’s Copyrighted Term Extension Act (1998), the U.K.’s Copyright and Rights in Performances Regulations (2014), the European Copyright Directive (2014), and reactionary court cases like Belgium’s Deckmyn V. Vandersteen.

Thus my dissertation is interested in the stories companies and conglomerates tell, in line with Bose and Lyons’ own “interest[…] in the stories corporations tell about themselves and the ways that they weave corporate history into the larger narratives of communities and nations as a means of consolidating and justifying their practices” (3). But most importantly, I’m interested, as Bose and Lyons phrase it, in ensuring that those narratives are reliable, that they hold “mimetic accuracy” (3). In many ways, that is at the heart of my dissertation’s agenda: to detail the nature of Disney’s economic “storytelling,” and just what that means for folklore, which is largely regarded as belonging to a public collective, part of a creative or digital commons (Morell), similar to Britain’s economic Commons, and their legal definition of common land. And most importantly: the reason I’m even presenting these findings for a Digital Humanities course, and here on this blog with graphs I’ve previously worked on, is because I believe data visualization can elucidate/clarify those stories and how they are told.

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