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To demonstrate the cool sorts of data mining you can do, I decided to play with the new twitter transforms. I've produced some really nice graphs that demonstrate the power the views in Maltego can bring to your data analytics.
What I did was to start off with a phrase "@singe". I then transformed that to tweets. This showed all recent tweets to or about me. From there I transformed the tweets to twitter affiliation (i.e. a twitter user). Then for each of those users, I ran the 'tweets to' and 'tweets from' transforms. This gave me a nice first go at the networks surrounding me. Then for all tweeple who were referenced more than once, I ran the same 'tweets to/from' transforms. With the centrality data mining view, I could quickly see which tweeple were referenced several times and continued running the transforms against the most highly referenced people. I soon ran out of the 75 transforms allowed in the community edition.
From this data, I have a good idea of the twitter communication network that surrounds me. With the centrality view, you can immediately see there are two distinct networks, the South African twitter-sphere, and the Security twitter-sphere.
This is an interesting view. I know I operate within these two networks and the people in the one don't talk to the people in the other, but to have it represented so clearly is interesting.
Next, I switched to the edge-weighted view which looks at the number of incoming and outgoing connection of each entitiy. This provided some insight into how these networks are structured. It is easy to see that the South African twitter-sphere is far more connected, the people there share a common group of friends, it's also easy to pick up the central nodes of the network, stii features quite prominently. The security network on the other hand is far more distributed and far less connected, with the central players much less easier to spot.
The other piece of information this has provided are any people I should be following that feature prominently in either of the networks. For example, Tanya de Ville, Sheena Gates, Wogan May, Nick Jackson and Gabrielle Rosano are all people I don't currently follow but maybe should. Although, I tend to follow people I know personally in the South African network. On the other hand, I don't know most of the security tweeple personally and it tends to operate on more of a meritocracy, so this has given me some good ideas of other security tweeple I should follow; Andrew Hay, Marcus J. Carey, Thomas Nicholson and Rob Fuller.
I should add a disclaimer that I had Maltego set on max speed so it only returned 12 results, this means these graphs are very temporal based, tweple that were making more noise at the time I ran them featured more prominently. Also, I was using the community edition, and was limited to 75 transforms. Thus, don't take this as a personal slight if your name doesn't show up.
My intention is to show how Maltego's views can be used for quick visual analysis of interrelated data sets. With the inclusion of local transforms, I'm excited about the possibility of using this for all sorts of things, nessus/nmap output, firewall rules, customer info data sets etc. Nice work Paterva.
Tracked: Dec 19, 13:49