Along with Natalie Kretschmer, Theodore Manning, Faihaa Khan, and Nuraly Soltonbekov, I worked on Annotating Dracula. Teddy suggested Dracula by Bram Stoker (1897), and being spooky season, it was futile to resist the book’s thrall. From our first meeting as a group, it was clear we are all interested in different aspects of Dracula and annotating, so we agreed to take differing yet complementary approaches to the project. Natalie was most interested in exploring the vibrant meme history of the text on Tumblr, especially surrounding the novel’s resurgence in popularity with the Dracula Daily substack. Teddy started with providing historical context and the evolution of tropes within the text. Nuraly wanted to dispel some of the myth and exoticization that is rampant in much of the story. Faihaa was a first-time reader and created annotations that included her reactions to the story, definitions of words or concepts that aren’t clear, and also gif reactions. I chose to think about the text as an object and was focused on using digital tools for text analysis. We all agreed from the beginning that our annotations would be a mix of so-called high-brow and low-brow, and that we wanted to make our annotations fun!
For platforms, we chose between Manifold and the CUNY Commons. The main advantage to Manifold would have been that we could ingest the story directly from Project Gutenberg and immediately get to annotating. However, we knew our annotations would include a mix of text and images, videos, and gifs. Manifold’s annotation tool only allows text annotations, so our other annotations would have to be added separately as digital objects, creating a disjointed user experience, and perhaps a hierarchy within our annotations, which we ultimately decided was a deal breaker. Furthermore, the diary/journal entry format that predominates the text felt very well suited to the blog-default format of the Commons (which uses WordPress). I also was very intrigued by the idea of annotating as layering, and thinking about how Dracula is a story of layers–layers of diary and journal entries, letters, telegrams, newspaper clippings, etc. As such, I convinced my group I could come up with a tagging schema to tease out these layers, and thus our project lives on the Commons.
From Project Gutenberg, I copied each segment of text into a blog post, which I numbered sequentially. Each post corresponds to one diary/journal entry, letter, telegram, newspaper clipping, etc. I respected all breaks in the story as published, with the exception of journal entries that included “later” posts within the same date–these appear grouped together in one post. However, when a journal entry of the same date was separated by a chapter break, this break was maintained and I created two posts. Every post is categorized according to the chapter it appears in. Furthermore, almost every segment in Dracula has a date, so I created both month and date tags, such that readers could see all of the content that happens within a certain month, or even on a certain date (there is a lot of time jumping back and forth in the text). I also created tags for journal and diary entries as a whole, as well as subtags for whom the diary or journal belongs to (Jonathan, Mina, Dr. Seward, and Lucy). Similarly, I created a tag for each letters, telegrams, and memoranda and notes, as well as correspondence tags for the senders and recipients; there are also tags for newspaper clippings and ship’s logs. There were also several segments of text that were correspondence that Stoker notes were not delivered to or received by the intended recipients, which I thought was fascinating and so created a tag for them. If I had more time, I would have loved to come up with more thematic tags like this.
Our annotations were created with Hypothes.is, and we decided we wanted them all to live together, so I ensured that every entry appears all on the same page. I also had to do some trouble-shooting to get all of the posts to appear in the correct order (I copied them in the order of the story, which put them in reverse chronological order as WordPress defaults to the newest post first). This was actually a real pain as every time I made an edit to a post, it took them out of order again, and I had to manually reorder the posts (and there are 188 of them!).
Building our site, tinkering with the CSS, and creating the categories and tags took a lot longer than I had anticipated, so I didn’t end up doing as much text analysis as I originally thought. I started with Google’s Ngram viewer and I wanted to see the usage of “vampire” in their corpus between 1887 and 1907 (10 years before and after publication) to see if this might show us the impact of his novel in literature. There was no discernible trend for these years, and when you look at all of the dates available for their corpus (1800-2019), the use of vampire doesn’t really take off until the late 90s and into the 00s. I then turned to Voyant to create a word cloud of the 75 most used words, excluding stop words. By far the most used word is “said”, and if you removed the character names, I honestly wouldn’t know this word cloud was from Dracula. It’s a bit generic, and I think I would need to spend a bit more time filtering to create something more meaningful or telling. Lastly I played around with the text using Python and the Natural Language Toolkit, namely using the .similar() function on various words that our group thought would be most interesting, e.g., vampire, blood, red, lips. See the About the Project page for more details.
Being an avid pinballer and having watched a lot of Dracula-related movies and TV shows over the Halloween season, I couldn’t resist including mentions related to these in my annotations. Also: Lots. Of. Gifs. I couldn’t help myself.
Overall I think our project has been a resounding success, and I think we’ve explored what is possible through annotation and how annotation can add to rather than distract from a text. Reading everyone else’s annotations got me really excited and helped me see different things I had overlooked in previous readings. The main drawback I see is that the Hypothes.is annotations are tied to the URL, and if you click on a category or a tag, it takes you to a page with a different URL, so you can’t simultaneously explore the layers and the annotations. However, even before our project was finished, a user cited us a source, which was incredibly exciting (see post 55). Hopefully we can be a source for other people just getting into or rereading Dracula.