I’m working on a paper on topical communities, and as part of that I’ve come back to this dataset to explore the social network that emerges through @ mentions.
To start with, I looked at the social network that emerges when we look at the people on the list.
This network is pretty densely connected, with the exception of two users on the list. You can see their nodes floating away in the image below:
The network graph that emerges from all the tweets connected is really busy, but may show who the most engaged users are.
There’s just too much information here, so I started filtering it by eliminating nodes that had fewer than a specified minimum number of connections. Because of the dataset available, non-news-influencer nodes cannot be connected to each other. Thus, I was specifying how many influencers needed to mention a user for them to make it into the graph.
Setting the minimum to two dramatically reduces the size of the graph. Many of the nodes remaining are also well known, for example @jack and @alyssa_milano.
We can also see popular websites, like @techcrunch and @boingboing as well as @google (not surprising given how often google showed up in the earlier visualizations of tweet content.
I find the graphs for minimum 8+ fascinating – I think they start to show who influences the influencers.
Eventually, of course, we get back to our original graph.