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I continue to be amazed what you can do with Google docs!
Concerning the by-week tweeting and blogging: is there anything in the contents to give a hint as to the explanation? A quick hypothesis: to begin with people used twitter and to give instant views/reactions, with a significant number of tweets being about the course; more considered blog posts on the state of HE took a bit longer and after a few posts people are starting to have expressed their extended ideas and made their initial “about the course” posts; but twitter is stable from week 3 as baseline discourse.
Looking at the adjacency matrix made me wonder whether you have tried clustering and re-ordering. I’m used to the way heatmaps work like this in R (although not using SNA-type modularity calcs) but I also found this rather neat visualisation made using D3 – http://bost.ocks.org/mike/miserables/ .
Content is increasingly on my mind. As well as shuffling around your Text-Mining-Weak-Signals (pre-plunge) I’ve been wondering about Speech Act Analysis as a way of delving in a bit more.
I had come across the D3 adjacency network from this presentation: A Fast and Dirty Intro to NetworkX (and D3) which uses this modified example. The sort is very nice, would be better if there was a control for the heatmap. The presentation has some other techniques and is worth an explore.
#LAK13: Recipes in capturing and analyzing data – Twitter Jisc CETIS MASHe
[…] CFHE12 Analysis: Summary of Twitter activity […]
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