Having dusted off and reversioned my Twitter emergent social positioning (ESP) code, and in advance of starting to think about what sorts of analyses I might properly start running, here’s a look back at what I was doing before in terms of charting where particular Twitter accounts sat amongst the other accounts commonly followed by the target account’s followers.
No longer having a whitelisted Twitter API key means the sample sizes I’m running are smaller than they used to be, to maybe that’s a good thing becuase it means I’ll have to start working properly on the methodology…
Anyway, here’s a quick snapshot of where I think hyperlocal news bloggers @onthewight might be situated on Twitter…
The view aims to map out accounts that are followed by 10 or more people from a sample of about 200 or so followers of @onthewight. The network is layed out according to a force directed layout algorithm with a dash of aesthetic tweaking; nodes are coloured based on community grouping as identified using the Gephi modularity statistic. Which has it’s issues, but it’s a start. The nodes are sized in the first case according to PageRank.
The quick take home from this original sketchmap is that there are a bunch of key information providers in the middle group, local accounts on the left, and slebs on the right.
If we look more closely at the key information providers, they seem to make sense…
These folk are likely to be either competitors of @onthewight, or prospects who might be worth approaching for advertising on the basis that @onthewight’s followers also follow the target account. (Of course, you could argue that because they share followers, there’s no point also using @onthewight as a channel. Except that @onthewight also has a popular blog presence, which would be where any ads were placed. (The @onthewight Twitter feed is generally news announcements and live reporting.) A better case could probably be made by looking at the follower profiles of the prospects, along with the ESP maps for the prospects, to see how well the audiences match, what additional reach could be offered, etc etc.
A broad brush view over the island community is a bit more cluttered:
If we tweak the layout a little, rerun PageRank to resize the nodes (note this will no longer take into account contributions from the other communities) and tweak the layout, again using a force directed algorithm, we get a bit less of a mess, though the map is still hard to read. Arts to the top, perhaps, Cowes to the right?
Again, with a bit more data, or perhaps a bit more of a think about what sort of map would be useful (and hence, what sort of data to collect), this sort of map might become useful for B2B marketing marketing purposes on the Island. (I’m not really interested in, erm, the plebs such as myself… i.e. people rather than bizs or slebs; though a pleb interest/demographic/reach analysis would probably be the one that would be most useful to take to prospects?).
If we look at the celebrity common follows, again resized and re-layed out, we see what I guess is a typical spread (it’s some time since I looked at these – not sure what the base line is, though @stephenfry still seems to feature high up in the background radiation count).
For bigger companies with their own marketing $, I guess this sort of map is the sort of place to look for potential celebrity endorsements to reinforce a message (folk following these accounts are already aware of @onthewight because they follow @onthewight) as well as potentially widen reach. But I guess the endorsement as reinforcement is more valuable as a legitimising thing?
Hmm…
Just got to work out what to do next, now, and how to start tightening this up and making it useful rather than just of passing interest…
PS A related chart that could be plotted using Facebook data would be to grab down all the likes of the friends of a person of company on Facebook, though I’m not not sure how that would work if their account is a page as a opposed to a “person”? I’m not so hot on Facebook API/permissions etc, or what sort of information page owners can get about their audience? Also, I’m not sure about the extent to which I can get likes from folk who aren’t my friends or who haven’t granted me app permissions? I used to be able to grab lists of people from groups and trawl through their likes, but I’m not sure default Facebook permissions make that as easy pickings now compared to a year or two ago? (The advantage of Twitter is that the friend/follow data is open on most accounts…)
OUseful.Info, the blog... 