Boston is a reminder that: Open source warfare doesn't ever go away.
It can be revived with a terrible suddenness, as we saw at the Boston Marathon.
The grievances and motivations for attacks never die. They can always find a corner of the Web to fester and grow, in groups too tiny to ever control.
The information needed to conduct attacks will always be available, and with each round of incidents, the information improves through testing.
Due to excessive industrial urbanization, the targets both in terms of people and infrastructure are thick on the ground.
Any single attack has the potential to ignite a series of additional attacks by other groups with similar, but different, motives.
Due to technological and behavioral factors, the quality of the attacks can better very quickly once a a conflict ignites. Weeks can yield significant progress instead of years.
I received an interesting bit of commission work earlier today – would I help someone plow up a bunch of astroturf. It sounded like an interesting test of my expanding social network analysis skills, so I agreed to take on the task.
I was given a list of six Twitter accounts. One had 2,000+ friends, two had 1,000+ friends, one had 500+ friends, and then there were two very small accounts that were suspected to be coordinators or handlers. Numbers like that are clearly beyond the query limits for Maltego, so the solution would have to be Gephi. I recently published my savetwit shell script, which exercises the python twitter package in order to collect as much information as possible on Twitter accounts. The process kicks out a couple of csv format files suitable for use as edge files in Gephi.
The accounts in question proved to have 4,300 friends and 5,200 links. This is labeled ‘anonymous’, it isn’t anything to do with Anonymous, it’s just a group that I don’t want to spook by mentioning their names.
. . . . . .
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And here we have it – the tricolor battleground, with yellow/green on one side, and red on the other. The single red and two blue dots are what modularity shows us. I suspect that if these accounts were isolated, entered into Maltego, and manually colored for allegiance that this would be a three way tussle, and that the names in this portion of the graph are the ones that are lobbing verbal grenades at each other on a nightly basis.
Yesterday after I posted Exploring e-International Relations I kept digging. I visited the LinkedIn profiles I had and found half a dozen additional people by using the “also viewed” column on the right. One of them responded to my request to connect, so I should be able to see the whole group fairly soon.
Having had such poor luck yesterday with an automatic method I went at it in manual mode – I looked through the followers of a few of the smaller accounts and I noticed a ‘team members’ list on the @e_IR role account. This put me in a position to see what they’re up to:
First, I loaded the e-IR-Twitter file into Maltego. This file contains both e-IR people and their close associates, so I selected just the staff by picking the entities tagged with a yellow star, one of five available ‘colors’ that can be used to tag groups of entities. Once I had the right group selected I pulled all of their tweets, and then all of the associated hashtags. The clusters around the edges are a Twitter account, it’s tweets, and then a more sparse sprinkle of hashtags.
Collaboration, Peer production, Peer networks, Crowdsourcing….the more I read about these topics the more I understand the enormous opportunities for social development and governance that are already out there. But at the same time, there are some new challenges to address.
For every new concept introduced in Government 3.0 I have the same reaction. First, I am all confused about it. Second, I start to understand it, but at the same time it always looks kind of utopian or not really applicable in the government field. And finally, I find some practical examples and ideas that allow me to think that these concepts are in fact both interesting and feasible.
When I was checking out the Think Tanks & Civil Societies Program I noticed e-International Relations – the world’s leading website for students of international politics. They had an About page similar to that of Wikistrat, listing all of their volunteer editors and some additional information on them.
Last night I entered most of that information into e-IR-base, a Maltego graph. Those who want to follow along can download the graph file, get the free Maltego Community Edition, and do a portion of the things I do with it. The free version has very limited access to Paterva‘s transform servers, so I will provide the necessary intermediate files.
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This is a top level view of the e-IR graph. What I say next presumes some knowledge of hands on work with Maltego.
The lavender dots are Person entities – a place for a first and last name, and like every entity you can makes notes and attach files to it. The blue dots at the upper right are URL entities and they contain links to an editor’s profile on the official site. Not everyone has a profile – this seems to be for people who produce their own content as well as work as editors. The five green dots are Twitter accounts, the five blue dots with an orange dot in the middle are LinkedIn profiles and an entity for the domain itself.
Maltego provides different types of entities, but here at the start we are only using Person, Domain, URL, and Phrase. Maltego provides a way to group different types of entities using colored stars – blue, green, yellow, orange, and red. This is useful for searching and organizing tasks – if you run a transform that starts with the five Twitter accounts shown here, but gets back over a thousand responses, how do you spot your originals?
Having had some success in domestic policy decision making, with Progressive Congress News being the final result, I thought I would see if there was anything that needed doing in the realm of foreign policy. Wikistrat, [allegedly]the world’s first Massively Multiplayer Online Consultancy (MMOC), was something that was immediately visible once I graphed my personal contacts. I wrote six posts about them as I mapped their network.
Foreign Policy Process – I graphed my contacts in the foreign policy field, I found a bunch of the top organizations and subscribed to their news feeds, and then I noticed the Wikistrat group.
Foreign Policy Organizations & Individuals – two of Wikistrat’s 156 experts were LinkedIn contacts for me. I explored the subset of members who had Twitter accounts and speculated as to what additional information could be learned about them with just social media as a starting point.
Exploring Wikistrat With Maltego – Starting with the Twitter accounts of the roughly two dozen Wikistrat members, I extracted the information from current discussions of one of the busier members, hunting for signs of issue focused communities of which the Wikistrat analysts are members. I didn’t make any great discovery, this is just an exposition on the process I used.
Wikistrat’s Analysts & Friends – I extracted the list of well connected contacts for the identifiable analyst Twitter accounts. A small connected network was revealed, but it broke down as soon as I removed the organizational role accounts that were found. This fits my expectation – Wikistrat analysts have rich interactions, but they didn’t self-organize with Twitter as a base and it seems likely they don’t participate in public theater in support of their conclusions.
Wikistrat Full Network As Of 3/30/2013 – I finally had a full Wikistrat map – the names of every member and their associated profile on the company web site. Some had LinkedIn or Twitter accounts, with the professional network being found for 40% of members and Twitter accounts being located for 20%. Overlap of LinkedIn and Twitter accounts was rare – only 2% – 3% show this pattern. I applied Named Entity Recognition to the profiles on both Wikistrat and LinkedIn. I thought I might be able to identify geographic clusters, employment clusters, or education clusters. The Wikistrat profiles are very regular in their layout, but quite resistant to the efforts of the Alchemy and OpenCalais NER products. A hand coded script with a little regex could be applied to the Wikistrat profiles, but I have not continued down that path.
Hashtags & Humans – I retrieved the most recent dozen tweets for all of the Wikistrat analysts, then extracted the hashtags they were using. I found that there were some hashtags that were congregation points, but that it was more common for there to be clusters of related tags.
Maltego provides a slider that allows four different volumes of information to be returned from a transform, their term for a query. The settings are 12, 50, 255, or 10,000. Twitter related transforms often stop at 100 entities, a limit enforced by Maltego publisher Paterva’s servers. Named Entity Recognition services are tuned for actual language and don’t perform terribly well on bodies of text with specific formats, nor were they all that useful in terms of picking out entities from tweets. Once tweets were available, hashtag extraction produced useful information, but there are performance constraints here as well.
Technical performance considerations aside, this process did reveal useful information, and some old wisdom from noted social network analyst Yoga Berra are still quite applicable today:
Ariana Huffington: “the decision-makers are not acting in the best interests of the public”
Voice Over: “socio-economic evolution out of synch with natural evolution”
Joichi Ito: “frugal engineering happens in the absence of abundance”
Many good endeavors still working in silos. Sharing and cross fertilization not there yet.
Those who have been sideline by power now have ability to by-pass power and connect to all.
Published on Apr 10, 2013
What will the world look like in 50 years? The problems facing our world are so large that they demand disruptive thinking. We don't have time to think in incremental terms. It's time to challenge the status quo, and dare to imagine what we can do.
