Noowit is a new curation and publishing platform that allows you to do on the web something very similar to what Flipboard allows you to do with your smartphone or tablet. You can curate a beautiful-looking web magazine, by selecting content from its internal news discovery engine or by clipping any content you find on the web with the dedicated NOOWIT bookmarklet.
On the backend you can select individual topics, authors and specific sources you want to subscribe to, to keep yourself informed. You can provide specific RSS feeds or import your collection of RSS subscriptions. You can create multiple content sections inside a magazine and when you add new content you can easily decide in which section it is going to end up. A swift navigation scheme provides almost seamless integration between the excerpted content that appears in the magazine and the full, original resource that you can navigate to without losing touch with the rest of the magazine. NOOWIT magazines can be set to be public or private and they can be viewed across devices and screen of all sizes. Like on Flipboard it is now possible to edit, modify or add to content that you pick and select to be added to your magazines.
My comment: NOOWIT easily creates great-looking digital magazines of your selected articles and resources. It is a great tool for anyone wanting to create easily a “splashy” curated digital magazine that looks great across devices with the minimum effort possible.
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.
Many large Web companies have failed to adopt a decades-old encryption technology to safeguard confidential user communications. Google is a rare exception, and Facebook is about to follow suit.
Revelations about the National Security Agency's surveillance abilities have highlighted shortcomings in many Internet companies' security practices that can expose users' confidential communications to government eavesdroppers.
Secret government files leaked by Edward Snowden outline a U.S. and U.K. surveillance apparatus that's able to vacuum up domestic and international data flows by the exabyte. One classified document describes “collection of communications on fiber cables and infrastructure as data flows past,” and another refers to the NSA's network-based surveillance of Microsoft's Hotmail servers.
Most Internet companies, however, do not use an privacy-protective encryption technique that has existed for over 20 years — it's called forward secrecy — that cleverly encodes Web browsing and Web e-mail in a way that frustrates fiber taps by national governments.
Lack of adoption by Apple, Twitter, Microsoft, Yahoo, AOL and others is probably due to “performance concerns and not valuing forward secrecy enough,” says Ivan Ristic, director of engineering at the cloud security firm Qualys. Google, by contrast, adopted it two years ago.
What does Big Data mean in the context of disaster response? Big (Crisis) Data refers to the relatively large volume, velocity and variety of digital information that may improve sense making and situational awareness during disasters. This is often referred to the 3 V’s of Big Data.
Volume refers to the amount of data (20 million tweets were posted during Hurricane Sandy) while Velocity refers to the speed at which that data is generated (over 2,000 tweets per second were generated following the Japan Earthquake & Tsunami). Variety refers to the variety of data generated, e.g., Numerical (GPS coordinates), Textual (SMS), Audio (phone calls), Photographic (satellite Imagery) and Video-graphic (YouTube). Sources of Big Crisis Data thus include both public and private sources such images posted as social media (Instagram) on the one hand, and emails or phone calls (Call Record Data) on the other. Big Crisis Data also relates to both raw data (the text of individual Facebook updates) as well as meta-data (the time and place those updates were posted, for example).
Ultimately, Big Data describe datasets that are too large to be effectively and quickly computed on your average desktop or laptop. In other words, Big Data is relative to the computing power—the filters—at your finger tips (along with the skills necessary to apply that computing power). Put differently, Big Data is “Big” because of filter failure. If we had more powerful filters, said “Big” Data would be easier to manage. As mentioned in previous blog posts, these filters can be created using Human Computing (crowdsourcing, microtasking) and/or Machine Computing (natural language processing, machine learning, etc.).
Click on Image to Enlarge
Take the [first] graph, for example. The horizontal axis represents time while the vertical one represents volume of information. On a good day, i.e., when there are no major disasters, the Digital Operations Center of the American Red Cross monitors and manually reads about 5,000 tweets. This “steady state” volume and velocity of data is represented by the green area. The dotted line just above denotes an organization’s (or individual’s) capacity to manage a given volume, velocity and variety of data. When disaster strikes, that capacity is stretched and often overwhelmed. More than 3 million tweets were posted during the first 48 hours after the Category 5 Tornado devastated Moore, Oklahoma, for example. What happens next is depicted in the [second] graph below.
Humanitarian and emergency management organizations often lack the internal surge capacity to manage the rapid increase in data generated during disasters. This Big Crisis Data is represented by the red area. But the dotted line can be raised. One way to do so is by building better filters (using Human and/or Machine Computing). Real world examples of Human and Machine Computing used for disaster response are highlighted here and here respectively.
A second way to shift the dotted line is with enlightened leadership [third graphic]. An example is the Filipino Government’s actions during the recent Typhoon. More on policy here. Both strategies (advanced computing & strategic policies) are necessary to raise that dotted line in a consistent manner.
See also:
Big Data for Disaster Response: A List of Wrong Assumptions [Link]
“When rare events at the scale of Hurricane Sandy happen, we expect them to leave an unquestionable mark on Social Media activity.” So the authors applied the same methods used to produce the above graph to visualize and understand changes in behavior during Hurricane Sandy as reflected on Foursquare and Twitter. The results are displayed below .
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“Prior to the storm, activity is relatively normal with the exception of iMac release on 10/25. The big spikes in divergent activity in the two days right before the storm correspond with emergency preparations and the spike in nightlife activity follows the ‘celebrations’ pattern afterwards. In the category of Grocery shopping (top panel) the deviations on Foursqaure and Twitter overlap closely, while on Nightlife the Twitter activity lags after Foursquare. On October 29 and 30 shops were mostly closed in NYC and we observe fewer checkins than usual, but interestingly more tweets about shopping. This finding suggests that opposing patterns of deviations may indicate of severe distress or abnormality, with the two platforms corroborating an alert.”
Click on Image to Enlarge
In sum, “the deviations in the case study of Hurricane Sandy clearly separate normal and abnormal times. In some cases the deviations on both platforms closely overlap, while in others some time lag (or even opposite trend) is evident. Moreover, during the height of the storm Foursquare activity diminishes significantly, while Twitter activity is on the rise. These findings have immediate implications for event detection systems, both in combining multiple sources of information and in using them to improving overall accuracy.”
Now if only this applied research could be transfered to operational use via a real-time dashboard, then this could actually make a difference for emergency responders and humanitarian organizations. See my recent post on the cognitive mismatch between computing research and social good needs.
Curator Skills
• Sense-making: the ability to determine significance
• Social intelligence: the ability to connect with others in a deep way
• Adaptive thinking: the ability to come up with novel solutions
• Cross-cultural competency: the ability to operate in new contexts
• Computational thinking: ability to think abstractly and make data-driven decisions
• New media literacy: the ability to assess new media critically and use itappropriately
• Transdisciplinarity: ability to understand concepts across a range of disciplines
• Design mindset: the ability to understand how the physical environment impactsthinking and make conscious choices in using it
• Cognitive load management: the ability to filter information
• Virtual collaboration: the ability to be a productive part of a virtual team
