So this is the blog post where I admit that I am finding this course to be more and more challenging. As we started out defining the field and then discussing the state of DH, whether that be public history, digital scholarship, etc., I was feeling very good about the readings and discussion. I found all of the readings intriguing and interesting, and I was able to engage with the topics and think about them in the context of my research. Now that we’ve moved on to the methodology portion of the course, I am having a much harder time. When Lincoln mentioned the quadratic equation last week while we discussed text analysis and topic modeling, my brain sort of exploded and oozed out through my ears. I am not a numbers person, a math person, or a statistics person, so learning the nuts and bolts of these different methodologies is hard for me to wrap my mind around.
This week’s readings on networks did not make me feel any better. Combined, the readings provide a detailed and thorough introduction to and applications of networks. Out of the articles, blog posts, and textbooks, the one I got the most out of was both sections of Scott Weingart’s “Demystifying Networks.” As it was the first reading of the week it gave me a much more detailed look at networks than I previously had in Clio 1, and I realized, as I became aware of last week with text analysis, that this is much more complicated than I originally thought.
In the first paragraph of his article, Weingart points out that any data can be studied as a network, but states that this is a dangerous idea for two reasons: 1) While networks can be used on any project, they should be used much less often than they are, and 2) methodology appropriation is dangerous. Weingart then spends the rest of the article discussing the theoretical aspects of networks and the inherent assumptions that various network methods have, both of which are necessary in order to prove his second reason for why using networks can be dangerous. My quibble is that Weingart never specifically articulates what type of projects would benefit most from network analysis, and where such analyses have been most productive and useful. The rest of the readings for this week include several that demonstrate applications of network analyses, including Elijah Meeks’s posts “Visualization of Network Distance” and “More Networks in the Humanities or Did Books Have DNA?” I would have liked Weingart, who has extensive experience working with networks, to point out some projects he feels best use networks, and other projects he thinks don’t need to utilize such analysis.