Networks and Narratives

Apart from branching narratives, as commonly found in Choose Your Own Adventure books and in interactive fiction, there is no necessarily obvious way to convert a narrative into a network: unless, of course, what one has mind is a rather thin narrative in which every node only has a relationship to the node before it and the node after it, which is entirely possible, but not all that interesting. That doesn’t mean there aren’t a variety of ways to derive networks from narratives, but they are, as the verb recognizes, derivative, though often quite compelling.

The first way that most analysts turned to was to re-create studies of actual social networks in fictional social networks: determining the relationship between characters was either something done by hand, based on the insight of the analyst, or something automated: if two characters appeared in the same scene in a dramatic work or the same page or scene or chapter in a novel, then they could be said to be connected. The depth or nature of the link was something that could either be qualified or quantified: e.g., the more scenes two characters appeared in together, the stronger the link between the two. This simple method offered some unusual insights. In one study of Greek and Irish mythic narratives, the analysts found that the social networks in the Irish narratives were more like actual social narratives than those found in the Greek narratives.

I have never seen such a network inverted, so that events are connected by the number of characters they have in common, but someone must have done it, and I wonder what it revealed?

Another method for deriving a network from a narrative is to determine all the locations involved and to map them and then create a network of where characters travel or what happens at those locations.

NYPL’s Networked Catalog

I am someone who fell in love with and was awed by card catalogs, first at my regional public library, and then later at the various universities I attended. I loved the idea of walking down rows of small drawers, each containing hundreds of books, and the sense of power having access to that much information suggested. I never really minded flipping through the cards, and I always looked forward to that moment of serendipity when, while searching for something else, I came across an intriguing title or description — it’s also what makes browsing in book stores or wandering among library stacks so exhilarating: there’s what you think you want to know and then there’s this … this other thing which maybe, just maybe, is exactly what you wanted to know, only you didn’t have the right words for it, didn’t know how to seek it out, weren’t sure it even existed. And yet there it is, on a care-worn card.

Computer catalogs are not the same, but that doesn’t make [the kinds of interesting things the New York Public Library is doing][nypl] with its catalog any less interesting. Quite the opposite. Given that computer catalogs cannot offer the kind of serendipity that card catalogs can, they must seek out those forms of serendipity that they alone can proffer and find ways to highlight that, so that entirely new generations of library users, either in person or on-line, can fall in love with the possibilities of information to delight us, if only in its sheer, and sometimes overwhelming, aggregation.


Counting Things in Texts

*This is one of those posts that probably deserves a fuller version, something I might consider submitting to ProfHacker, but I’m in the middle of a bunch of other work right now, so it’s going to be shorter than I like.*

Two recent posts by non-scholars have used two practices[^1] that are emerging as conventions within the digital humanities: one is counting unique words to get a sense of vocabulary and the second is counting the number of times characters in a text appear together in scenes.

Matt Daniels counts words in [“The Largest Vocabulary in Hip Hop”][hh], and, thanks to an astute commenter, uncovers that at least one rapper purposefully minimizes his vocabulary in order to maximize sales: an interesting parallel here would be to examine political discourse of various public figures to see how appeals to the oft-lauded common man might be realized by vocabulary.

Ben Blatt counts the co-occurrences of characters in scenes in [“Which Friends on Friends Were the Closest Friends?”][ff]. Like Daniels, Blatt is upfront about his method: “To determine which characters shared scenes, I downloaded transcripts of all 236 episodes … If a character spoke a line in a scene, I marked him or her as present.” The results are interesting for those familiar with the show, but, as my wife noted, few undergraduate students would be familiar with _Friends_, but one could do this with a program with which they were familiar. Perhaps the most famous example of this kind of counting characters co-occurring in scenes — not to be confused with _Comedians in Cars Getting Coffee_ — is Franco Moretti’s [“”Network Theory, Plot Analysis.”][fm] (see below for conventional reference), wherein he uncovers that the compelling nature of _Hamlet_ may very well be that Hamlet and Claudius are both central characters, with Horation a close third — I saw Moretti give a version of this paper at the NEH seminar on network studies in the humanities organized by Tim Tangherlini at UCLA’s IPAM in 2010. (Oh, the debt I owe to Tangherlini!)


Moretti, Franco. 2011. Network Theory, Plot Analysis. _New Left Review_ 68: 80–102.

[^1]: There is actually a name for this, but I can’t think of it at the moment. *Method*? More coffee is needed….


No Need to Re-Invent the Sociological Wheel

File this under *no need to re-invent basic social science (or natural science or humanistic scholarship for that matter)*: it turns out that all the grand pronouncements about changes to human socialization are, first, just that, grand pronouncements, and, second, often get the basic social science wrong. In an [article on Medium][], Zeynep Tufekci sets the record straight on the matter of primary, or strong, ties and secondary, or weak, ties and their role in how humans negotiate their relationships with other humans.

[article on Medium]: