User-generated discontent: dissent and social media: presentation by Jack Bratich, faculty of journalism and media studies at Rutgers University.  Analysis of “Facebook revolutions” in terms of sovereign power vs. people power, and how social media are tools.

This is echoed in “Clive Thompson on The Breakthrough Myth,” an essay in the August 2001 Wired by Clive Thompson (who blogs at Collision Detection).  He credits Bill Buxton, “a pioneer in computer graphics who is now a principal researcher at Microsoft, with the “long nose theory” of innovation:  “Big ideas poke their noses into the world very slowly, easing gradually into view.”  Essentially, ideas that are good but insignificant can catch on, often in other domains, after a period of time.  Thus they have “surprising obviousness.”  In other words, upon the arrival of a novel idea or product, the evidence has been there.  To predict the future, Buxton recommends “prospecting and mining” fields of endeavor.  That word, “mining,” immediately suggests data mining, a technology only starting to be exploited–we see only the tip of the nose, so far.

The Blue Horizons model bears some resemblance to the decentralized structure of human brain processing:  there is some degree of localization, but scans of cognition representing the greatest human achievement show almost the whole cortex lighting up–a large neural network, which actually has high efficiency.

There is a new book (entitled The Filter Bubble) out about it.

A wise possibly-related comment about searching appeared on Slashdot:  “The lesson is that the existence of information and the ease with which it may be looked up are two different things. Dirt is just too easy to find, for the same reasons that gold is too hard: search engines’ evaluation of “importance” or “relevance” doesn’t always match any sane human’s.”

Flipboard & Aggregation Apps: the Future of Mobile Media?

Although it is described as “a site that crowdsources research,” it aims lower than published findings and allows discussion of what didn’t work, and job opportunities or other peripheral information, in contrast to the published paper.  That seems a little less focused than crowdsourcing, but potentially useful nonetheless.  Still there is the question of mechanism:  throwing a bunch of scientists into a pot is only the beginning; it’s how their thought claims are aggregated (not to mention the number of participants) that would distinguish this from existing networks and crowds.

Comparison has been drawn to Mendeley, which is a similar, slightly older effort.  Mendeley is more structured and built up from a reference manager like RefWorks or EndNote.  (Click on the “Features” tab.)