Two articles follow: one posits a seemingly global anti-US opposition, an Anti-American Network (AAN), and the other posits that political warfare is the answer to the Middle East portion of the problem. IMHO, both are worth considering. Further believe that, with respect to Boot & Doran's approach, (a) coverage needs expansion to cover all the opponents Hirsch posits and (b) political warfare is a necessary but not sufficient component of our response and an NCTC-centric structure is probably not the way to go. We already have policy in place to deal with these kinds of things but it probably needs revision in light of international and domestic politics. In my view, what we need is national leadership (read: POTUS and Congress) with the guts and principles of Britain's WWII leader Winston Churchill supported by an Executive Branch organizational structure combining the best features of their Special Operations Executive (SOE) and Political Warfare Executive (PWE), one authorized, directed, and capable of covertly, surgically and virtually “setting our adversaries ablaze.” Neither the currently tasked organization nor U.S Special Operations Command, or even the two together, is presently that structure.)
If you are looking for an effective tool to identify Twitter influencers in specific niches and regions of the world, here is a super handy new tool.
Twtrland is a new web app which allows you to easily find key influencers on many niche topics including the ability to identify those influencers based in specific geographic regions.
Try searching for a specific Twitter user by name and last name and check out the thorough profile that Twtrland builds for you. Very useful. Then try a city and drill down to find who are the influencers by using the filters on the left side. Finally try to search for one of the 60K skills already covered (too bad “Content Curation” isn't there yet).
From the official site: “Twtrland. It allows you to search Twitter by names, location and skills and surfaces a wide variety of insights, stats and useful pointers. It’s especially useful if you’re researching specialists (by country/location) as well as checking someone out (beyond the usual LinkedIn search).”
Free version available.
The PRO version allows for more search results, filters, the ability to collect profiles into separate folders, to export them, and to analyze fully the stats of any brand, keyword or user for $19.99.
My comment: Hard to beat. Great research tool allows you to rapidly find relevant influencers in a growing number of verticals. Easy to use. Very useful.
OpenTopic is a news curation service which allows you to aggregate, monitor and filter any number of sources and to publish and share your selected ones to you selected outlets: from your WordPress site, to your social media channels and to your email newsletter engine. Within OpenTopic you can create one or more “Topic” dashboards. These are essentially display pages that aggregate incoming fresh content from the sources you specify. You can jump from one Topic dashboard to the next at the click of your mouse. To curate stories you simpy select the ones that are relevant to your audience and you are provided with an editing module to modify and personalize the story content. At this point you can also select on which one of your outlets (Channels) that story will be published and you can customize the story differently for each one of them. There is even an option that allows you to set-up some form of automated curation, by giving you the option to set up a set of simple rules, which when match, will trigger the publishing of a news story. OpenTopic allows you to hook up to an extended number of possible Channels, making it easy for you to post from one location to your web site, RSS feed, social media and newsletter. Last but not least, OpenTopic integrates a full analytics service, capable of reporting and showcasing the performance of your curation work across stories and distribution channels. My comment: Excellent tool for social media and community managers, as well as web marketing specialists in need to support effectively the finding of relevant news on a topic and the easy publishing to different channels from a centralized platform. Easy to use.
In a production economy, value creation depends on land, labor and capital. In a knowledge economy, value creation depends mainly on the ideas and innovations to be found in people’s heads.
Those ideas cannot be forcibly extracted.
All one can do is mobilize collective intelligence and knowledge. If knowing how to produce and sell has become a basic necessity, it no longer constitutes a sufficiently differentiating factor in international competition. In the past, enterprises were industrial and commercial; in the future, they will increasingly have to be intelligent.
The intelligent enterprise stands on three pillars: collective intelligence, knowledge management and information and collaboration technologies and needs the vital energy of intellectual cooperation.
Managing collective intelligence implies a radical change that will naturally elicit a lot of resistance. But we’re talking about a social innovation. Once it is in place, once the resistance has subsided, no one will want to go back to the way it was! As always, the problem lies “not in developing new ideas but in escaping from the old ones.” Keynes.
One of the things I have done over the last six months has involved identifying and observing hive mind constructs in the real world. This happened in the context of examining the publicly visible process of foreign policy making. I wrote thirty three posts that are at least tangentially related to this pursuit. Hive mind constructs will eventually win out over point source propaganda, but it won’t be pretty to watch.
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Links and short descriptions of various sequential endeavors and their findings
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CONCLUSION:
Monolithic corporate forces heavily invested in the status quo are wrestling with networked humans and finding they face a sort of memetic Devil’s Snare. Their struggles may seem to be momentarily successful, but they are only educating their opponent as to their strengths and weaknesses.
The concept of the corporation didn’t really take off until the Catholic church relaxed usury laws three centuries ago. Compound interest depends on exponential growth and humans have pretty much hit the wall in terms of what our environment will support. Any one of climate change or peak oil could undo the perception that we are all consumers living in a conglomeration of free markets. Those two have arrived pretty much simultaneous with a financial sector meltdown and we are entering a period where our society will wind down to the Earth’s solar maximum. A value system based on exponential growth will not survive a disproof by counter example, and Mother Nature responds to neither paper injunctions nor heartfelt supplications.
Some of those networked humans are starting to realize that they need not tear down the corporatocracy by hand and they are already thinking about how and what to preserve. What role does a hive mind play in this? What role can it play when electrical power is intermittent and the supply chains needed for electronic devices are interrupted?
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:
