The concept of a network has become ubiquitous in current culture. Almost any connection to anything else can be called a network, but properly speaking, a network has to be a system of elements or entities that are connected by explicit relations. Unlike other data structures we have looked at–data bases, mark-up systems, classification systems, and so on—networks are defined by the specific relations among elements in the system rather than by the content types or components. The term network is frequently used to describe the infrastructure that connects computers to each other and to peripherals, devices, or systems in a linked environment. But the networks we are concerned with in digital humanities are created by relationships among different elements in a model of content.
(Introduction to Digital Humanities – Networks, by Johanna Drucker)
Read all of the required readings listed below and leave at least one annotation on at least two of the articles in our digitalstudies group on hypothes.is.
Read through your some of colleagues' annotations on the hypothes.is page for the digitalstudies group. Reply to at least two of them.
Skim through the tutorials listed below under "Readings", and choose one of the network analysis/visualization tools to try out on your own. (You can work individually or with others from class; please no groups larger than three people, so that everyone is able to have a hand in the technical work.) Do your best to have some kind of "result" to show off in class on Tuesday, but if you get stuck after putting an honest attempt in, don't sweat it. Regardless of how far you get, bring a list of questions or difficulties that you ran into, as well as the result you were able to produce.
If you don't know what kind of network data you'd like to visualize, here are a couple possibilities to get you started:
In class, we will show off our (preliminary) network analyses, offer feedback to each other, reflect on insights gained, and discuss strategies for improving/completing the work. Time permitting, I will demonstrate some of the ways I've been using other network tools ― namely web spiders ― to analyze federal government websites and monitor them for changes.
Following are three different visualizations of word frequency in President Trump's campaign speeches. Take a look at each one and discuss the unique things each visualization communicates about Trump's campaign rhetoric.
Here's another, more robust, bigram frequency network graph, from a collection of articles about Domain of One's Own.
Complete your network visualization project the best you can, and get it in shape for public presentation on the web (a blog post, an interactive map, on your domain, hosted elsewhere, ... whatever best fits your project). Share a link and a short description of your project in the #domains channel (even if it's hosted elsewhere) on Slack.
Be sure to do some work on your domain this week. That could involve posting the results of your network visualization project to your domain. Or if that project doesn't fit your site's theme or isn't in the shape you'd want it to be before sharing it more publicly, you can make other additions/changes/deletions on your site (perhaps following up on last week's blogging unit).
Thursday class meeting
Thursday's class will primarily involve showing off some final results, discussing what we learned about both the content studied and the value of network analysis, and reflect on possibilities for larger projects in the future (for those who might like to spend more time with network analysis on their domain or for a final project). We will also introduce Week 6's materials and activities (Representation).
Complete your self-assessment for Week 5 and add it to the document you created last week. Be sure to comment on the updates you made to your domain, and include links to your network visualization project and at least some of your annotations/replies.
For materials due Monday and Tuesday, see the Week 6 guide.
See tutorials and tools under "Readings" above.
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