[note: this is an activity I developed for my Smith College course “Doing Digital History” taught in the Spring of 2020]
Early American Newspaper Citations
- Download this data that I produced as a part of my research.
- It represents nearly the entirety of late 18th century North American newspapers’ citations to newspapers outside of North America. These are all aggregated based on city and weighted by the number of citations. It’s structured in five-year increments.
- Go to Palladio. Click start.
- Paste in the data from the above file. Click “load.”
- Click “Graph” on the header (in between “Map” and “Table”)
- Under settings, for “Source,” choose “City of Origin.” For “Target,” choose “City of Publication.” (it doesn’t actually matter which of these you choose for which). Tick “Highlight” for one of them.
- Tick “Size Nodes,” then according to “Sum of Weight.”
- You’ve just created a very simple network graph.
- Click “Timeline” below. Palladio will recognize “Year” as a temporal variable, and allow you to look at how the network looks during specified time periods.
- Note that the data is structured in five-year increments: 1755–1759, 1760–1764, etc. You can’t get more granular than that.
- How does this network of citation change over time?
- Click “Facet.” This will allow you to explore the data based on particular cities.
- How does Boston’s role in the network change over time?
Try it on your own with a different dataset. Use sample data about 18th-century Quaker networks from The Programming Historian’s tutorial for Python: edgelist; node list.
- Open up the edgelist and examine it.
- Copy-and-paste the edgelist into the sample screen on Palladio where you loaded the previous data.
- Under the Untitled table, click “Source.” Then click “Add a New Table.” Paste in the data from the node list. Click “done.” Do the same for “Target.”
- See if you can make a network graph from here.
- What does this graph tell you? What doesn’t it tell you? If you knew nothing about the history of 17th-century Quakerism, what would you conclude about it based on this data?
- What’s different about this dataset from the previous dataset on citations?