Initial Thoughts on Profiling @dirdigeng’s Friends Network on Twitter
Last week, Andrew Stott, Director of Digital Engagement in the Cabinet Office, announced his retirement date over Twitter:
At the time of writing, @dirdigeng follows slightly over two thousand folk on Twitter, so I thought I’d have a quick look at who the “players” are…
The network described is constructed as follows:
– nodes represent the people followed by @dirdigeng on Twitter;
– a directed edge from A to B means that A is following B.
In the first view (randomly layed out, using Gephi), we plot node size as linearly proportional to the number of dirdigeng’s friends who are following each of the other friends (that is, the in-degree of each node), and colour proportional to their total number of followers (including people not friended by @dirdigeng).
The colour mapping is non-linear – @Number10gov, @guardiantach and @mashable have significantly more followers that the other nodes – and is set via the spline control:
If we run the betweenness centrality statistic, and size nodes accordingly, we can see how the various parts of the network may be connected. (“Betweenness centrality is a measure based on the number of shortest paths between any two nodes that pass through a particular node. Nodes around the edge of the network would typically have a low betweenness centrality. A high betweenness centrality might suggest that the individual is connecting various different parts of the network together.”)
We can also run the modularity class statistic to try to partition the friends into small networks with a high degree of internal connections. Here’s what we get (click through on the image to see it in more detail):
Modularity groups help us understand the structure of the network in a bit more detail. I’ve started to think they might also be used to automatically generate a seeding set of people who form a highly interconnected community with an interest in a particular topic and from a particular stance.
As well as looking at the structure of the network, we can also create a search engine over the home pages declared in the Twitter bios of @dirdigeng’s friends. My thinking here is that this might provide a useful constrained search engine over sites engaged in social media and with an interest in “Digital Britain”.
The simplest custem search engine simply uses the URLs from the Twitter bios of folk followedd by @dirdigeng and adds them to a “Digital Britain” Google Custom search engine. However, one attractive feature of the Google CSEs is that you can also tweak the rankings by weighting results from different domains differently to give a “weighted” custom search engine.
As a quick experiment, I produced one weighted search engine where I set the score for each domain to be the normalised number of followers amongst @dirdigeng’s friends community. (That is, the domain score equalled the indegree of a node in the @dirdigEng friends network, divided by the total number of people in that network).
As you can see from the above, the results differ… Whether there is any improvement in the ranking of results is another thing. (There is also the question of how best to score, or boost, rankings based on networks stastics, and the extent to which rankings should be determined by friends network factors…)
It also strikes me that the modularity groups might also be used to inform the setup of a CSE. For example, separate modularity groups/classes may be used to define refinement label, allowing users to just search pages from members of a particular modularity class, or boost the results from those people.
And finally, I wonder whether we can mine the tweets of @dirdigeng’s friends, as well as those of @dirdigeng, to provide raw material for additional advice for searchers?