Wikistrat bills itself as ” is the world’s first Massively Multiplayer Online Consultancy (MMOC)”. This is the largest of a handful of hive minds I have studied that focus on foreign policy. The blowout from the Egyptian army takeover is something I have known was coming for about two weeks thanks to my subscription to NightWatch. I am curious to see who among their analysts was first discussing it on Twitter, and who else they involved.
Things like this go through a progression, first the locals and knowledgeable observers in country detect something in the works, then the the well connected analysts begin to talk, then the specialty reporting outlets, and finally the topic broaches mainstream news. Somewhere in the mix, around the time the specialty outlets begin running the story the blogosphere will also pick it up and start playing with it. A while ago I wrote a Maltego transform for the premier reputation tracking system, Klout. I dusted it off this evening and applied it to Wikistrat. Only four out of the thirty accounts I have identified were not registered for Klout.
Wikistrat Analyst Influencers
I have been looking at the Wikistrat social network for a while now and the Twitter contingent is not large, so I recognize most of them by sight, and many of their conversation partners from the various examinations I have performed. The first surprise that there were so few influencers that reached more than one member. I had assumed I would find at least some luminaries from the field, but this is not the case. It was also a bit surprising to see that there were no instances where one analyst influenced another. My take on that is that these guys don’t talk shop on Twitter.
Wikistrat High Degree Influencers
Thinking that this set was a high value source for foreign policy information I went through and manually separated them into organization role account and humans. The role accounts aren’t interesting in this context, since they are mostly broadcasters rather than conversation partners who would have influencers.
Wikistrat Analysts Influential People & Orgzanizations
Once I had just the people I collected second generation influencers.
Wikistrat Second Generation Influencers
And when I checked those influenced by the core group I found no feedback loop at all. I think there are two explanations for this. The first is that this is a large universe, many players, and we’d start to see feedback loops if we took another step back. The problem with this is … two steps is a lot in a professional environment. The six degrees of separation meme is based on a sociology study in the 1960s and it more or less holds up no matter how large the system goes. There is a study of the Microsoft Messenger network where they had a set of two hundred million people and the average path length was about six. A professional network ought to have something similar to the Erdős Number for mathematicians – with an average distance between two people being four to five hops.
The other explanation is a little easier to swallow – Klout is seeing interactions but foreign policy is dense and there is an industry specific jargon. If the system can’t interpret the content of tweets it is less likely to automatically select influencers, and our test set are people who aren’t manually adding influencers to their profiles.
Three Generations No Feedback
Circular layouts are a way to see who is in the middle of the action and who is on the edge. Here we see a core of actors and some small clusters on the edge.
Three Generations Circular Layout
I selected the inner circle, moved it to its own graph, and used a force directed layout. This particular phase didn’t provide a lot of new information but in general this is a place you’d linger if you had a new, complex network you were trying to understand, so I include it for the sake of completeness.
Three Generations Influencers Core
And finally we get a bit of a payoff. I recognize @texasinfrica, this one turns up in all sorts of discussions. The other eight are a couple of role accounts and a handful of new people.
Three Generations Nine New Players
What did we accomplish here? I can see a few things.
I’ve had this ability for a long time but tonight was the first time I’ve applied it to more than a few accounts for testing. This is a selection of all influencers, not just foreign policy sources, so we’re stuck with a manual slog if we want to constrain the results to just that sector. The Maltego transform servers are limping tonight, which I credit to the work created by the Egyptian coup, so I can’t get into what these guys are saying without putting them into my Twitter recorder. There are maybe two hundred accounts listed so it would be an overnight job to get them all recorded for the first time.
What comes next?
The Maltego Klout transform code can be adjusted to produce output suitable for Gephi with a very small amount of work. We could use the Klout number itself to weight Twitter accounts, we can graph the influencers or influencees, and we can pull topics and create an accounts to areas of expertise map.
The Klout API rules are much tougher than Twitter or LinkedIN, where you can just create an application at will. I had to explain what I was doing and they were really helpful – my account has ten times the API credits of the individual/desktop accounts, and they agreed that if I could show some unique uses involving Maltego they would work with users that needed the higher capacity. They also limit caching to five to seven days, but this not good for forensics work or for long term studies of how clusters of accounts change over time.
Once we work through the access and data retention issues there are some really cool things that can be done – like launching a brand new Twitter account and taking daily snapshots of friends and followers as well as the derived Klout attributes. This data could be used to feed Sentinel Visualizer or the Gephi streaming plugin, producing an animation of an accounts growth and expansion into new topics.
I suppose the first order of the day here is encapsulating anything that can be done with the API in both a Maltego transform and something to output CSV for use with Gephi and Sentinel Visualizer. I will do this, publish the code to my Github, and then pick out something to study and write it up here.