As you may imagine, the Internet is already abuzz with its reactions.

Gladwell's Tipping Point book described the power of "Connectors" - those people whose knack for making friends and acquaintances amass them social networks containing over a hundred connections. Connectors link us up with the world, he said. Others with special "social gifts" were described as either "Mavens" (aka "information specialists") or the powerful persuaders known as "Salesmen."

And yet, in his current essay, Gladwell doesn't apparently seem to think that those same types of personalities can impact the world when they use their "gifts" on social networking sites in order to enact social change. Revolutions, activism, protests and the like that take place via social media are not like those in the past, he says, because "the platforms of social media are built around weak ties." 

Weak ties aren't necessarily a bad thing, though, Gladwell explains:

"Twitter is a way of following (or being followed by) people you may never have met. Facebook is a tool for efficiently managing your acquaintances, for keeping up with the people you would not otherwise be able to stay in touch with. That's why you can have a thousand "friends" on Facebook, as you never could in real life. This is in many ways a wonderful thing. There is strength in weak ties...," he says.

"But weak ties seldom lead to high-risk activism."

High-Risk Activism Won't Come from Tweets, Facebook

To illustrate this point, Gladwell pitted historical protests , like those from the Civil Rights era against modern ones, like he "Save Darfur" Facebook movement and the Iranian elections with its accompanying "Twitter Revolution."

In the Civil Rights era, says Gladwell, the high-risk activism that took place was based on strong ties and close relationships. It was rife with danger and often met with violence. 

But today, the so-called activism that takes place on social networks isn't nearly as risky nor impactful. For example,  the 1,282,339 members of the "Save Darfur" Facebook page have committed an average of 9 cents each to the cause.  The next biggest Darfur charity on Facebook has 22,073 members, who have donated an average of 35 cents. "Help Save Darfur" has 2,797 members have have given, on average, 15 cents, Gladwell writes.

He explains that "Facebook activism" succeeds by "not by motivating people to make a real sacrifice but by motivating them to do the things that people do when they are not motivated enough to make a real sacrifice."

As for the Twitter revolution surrounding the Iranian elections? It was more of a product of shoddy Western journalism than any real activism. Gladwell cited Golnaz Esfandiari's article in "Foreign Policy" which stated, "Western journalists who couldn't reach--or didn't bother reaching?--people on the ground in Iran simply scrolled through the English-language tweets post with tag #iranelection. Through it all, no one seemed to wonder why people trying to coordinate protests in Iran would be writing in any language other than Farsi."

There are many more examples in the article itself, but they all point to the same conclusion: activism that takes place on social networks just isn't the real thing.

"We are a long way from the lunch counters of Greensboro," says Gladwell, referring to the historic moment on Monday, February 1, 1960,  when four college students sat down at the lunch counter at the Woolworth's in downtown Greensboro, North Carolina and ordered a cup of coffee - the example that kicks off the lengthy essay.

Do You Agree?

In the article, Gladwell takes on social media activists, including Clay Shirky, author of one of the social media movement's bibles "Here Comes Everybody" plus Andy Smith and Jennifer Aaker, whose new book called "The Dragonfly Effect: Quick, Effective, and Powerful Ways to Use Social Media to Drive Social Change" tells the story of how a Silicon Valley entrepreneur used social media to find a bone marrow match when he came down with leukemia.

Gladwell says that social media enthusiasts don't understand the distinction between this latter scenario and real activism:  "They seem to believe that a Facebook friend is the same as a real friend and that signing up for a donor registry in Silicon Valley today is activism in the same sense as sitting at a segregated lunch counter in Greensboro in 1960," he writes.

The article is already being criticized for missing the mark, most notably by David Helfenbein on The Huffington Post, who says the piece is "generationally insulting." Gladwell is saying that "older generations knew how to create real, palpable movements; younger generations simply know how to push buttons," says Helfenbein. "But Gladwell, younger generations can do both," Helfenbein explains. "They have: they were in the Facebook groups for President Obama and then they showed up by the thousands to the rallies and then they voted for him. And in the end, whatever you believe politically, Obama won. This was one significant, high-risk movement."

Of course, one could argue that voting for president isn't really all that dangerous - it's a movement, sure, but was it "high risk?" Perhaps it's Helfenbein who is missing the point?

For those that only skim headlines, the article and the accompanying analysis makes for a nice tweet: "Gladwell gets it wrong (link)." But to those who still read longer articles like Gladwell's essay (or heck, this blog post summarizing), there's definitely food for thought here.

Feel free to share yours in the comments.   

How does an Internet junkie, news organization, or political operative monitor rapidly evolving real-time events, from the crucial details to the bigger picture? More importantly, how can a data stream be turned into real-time action, reaching the people who need it, when they need it, and in a form they can easily digest?

Overview

The proliferation of real-time search engines and trend monitors (sometimes referred to as "listening platforms") has thus far done little to address problems of this scale and importance. This is because they fail to provide context -- i.e. show how a new piece of information is relevant to what we've seen before and where it fits in the space of possibilities and relationships.

For instance, if you are a programming director at CNN trying to discriminate between significant news and Internet memes, simply knowing that #iranelection is a trending topic doesn't tell you its relation to other topics or which communities are driving it -- both critical factors.

One promising area is data-oriented user interfaces: data and algorithmic analysis in the back-end and direct visualization and navigation in the front-end. This the next stage of social information, slicing and dicing, mixing and matching, interpreting and analyzing, completely on demand. In this new landscape, the data is the interface.

It's not just about sitting back and looking at pretty pictures. It is about setting aside stale UI metaphors and getting as bare-to-the-bone a human interface as possible for computation. The recently launched Wolfram-Alpha applies this principle to structured data. (Disclosure: I was a member of the core Wolfram-Alpha team and may continue to consult with Wolfram Research.)

Real-time data streaming offers similar possibilities and opportunities. In this vein, let's outline some basic ideas and methods for giving context to the streams.

The Computational History of #iranelection

At the most abstract level, history and computation are the same thing: the evolution of systems over time. Twitter has several remarkable properties that allow us to finally leverage this correspondence in tangible ways. The simplicity of its data, the openness of its system, and its extreme time resolution make it possible for us to detect atoms of history, those moments when something is triggered and society is reconfigured ever so slightly.

Look at the bandwidth plots below. They represent the relative volume of different Iran-related phrases on Twitter over time. Their most striking characteristic is how discrete and spikey they are: a tell-tale sign of an organic computational system.

The first pair of terms compares the bandwidth of "Ahmadinejad" and "Khamenei" mentions, respectively. The evolution of the uprising at the very highest level of social abstraction is shown with remarkable clarity: moving from a dispute over the election process involving Ahmadinejad (shown in pink) to a dispute over authority involving the supreme leader Khamenei (shown in red).

Not only do we get the gist of the evolution, we also see its details and relationships to other social sub-structures. For example, looking at the second plot, we see a co-relation between mentions of the Basij militia and the reports of deaths; and that initial uptick in Khamenei mentions corresponds to the uptick in Basij mentions, foreshadowing the later crackdown.

This idea of computational history applies to events that Twitter not only reported but shaped and hosted as well. A plot further down below compares the Twitter-centric discussion of #cnnfail to the distribution of Twitter proxy IPs that allowed information to continue to flow out of Iran. Is it a coincidence that these two terms merge smoothly together? And what about the big spike in mentions of proxy distribution coinciding with the first reports of violence?

Computing with Social Structures

Simply tracking the volume of various phrases gives us a sense of what is happening on the street, literally and figuratively. But that signal is but a shadow of a far more complex and intricate reality, an interwoven web of individuals and actions.

Twitter allows these social structures to become data structures by means of the "RT" convention. And this in turn allows us to perform extremely powerful computations on the social structures that underlie the flow of information.

Network layout algorithms are a familiar, powerful, and fascinating example. They self-organize in your computer to reveal self-organization in the real world. And that is exactly the kind of tool we need to test our hypothesis about #cnnfail.

The plot below shows the network of people who re-tweeted mentions of IP proxies, with those who had tweeted earlier about #cnnfail highlighted. We see not only significant overlap among the people involved but also a considerable structure in the relationships between them. We have captured a real community at the moment of its birth.

Remember this as you look at the next plot below. Here, we see the re-tweet network that formed around the top five Iranian tweets. Its structure shows a very different phenomenon, capturing the emergence not of a community but of an elite. Despite massive interest, or perhaps because of it, most people did not discover more than one of the top Iranians. The network simply grew faster than the information could naturally propagate. But a small inner circle did succeed in identifying core sources of information.

The final plot below shows yet another community structure, as well as a new algorithmic technique. This plot does not show the emergence of a new community but rather shows the appropriation of a new topic by mature political factions. This re-tweet network has formed around Iranian tweets that mention Obama. Using graph theory, we can computationally extract the sub-communities and then use that information to color the network. The large blue mass on the right is the conservative Twittersphere, while the other structures are a less-organized collection of mainstream or progressive news outlets.

Algorithms and Social Change

Will future Presidents express strategic goals in terms of Twitter graph theory? That is almost a certainty.

The purpose of these computations is two-fold: first, to contextualize information from across time and space in terms that are accessible to humans; and secondly, to distill abstract ideas into actionable form.

Twitter is a platform for achieving both of these purposes in human affairs: detecting networks of information propagation and erecting new networks to reshape emerging social computations. This is the core of Twitter's social and business value. If we were to play that age-old game of "Guess the business model," we'd look here first.

How to algorithmically discover and deploy novel social structures is perhaps the billion, or trillion, dollar question. With Twitter, the data and API are in place. And if the history of computation is any guide, once programming a system becomes possible, progressing from a hack to an application to a platform is only a matter of time.

Guest author: Kovas Boguta is a co-founder of Infoharmoni, a stealth startup building computable knowledge interfaces for real-time data sets. He just returned from last week's Personal Democracy Forum, where discussion about the Iranian uprising took center-stage.

We're moving beyond the days of a simple search box in which you type a query and get a list of results. Today, companies are trying to build a smarter web - one that understands what things are, how they relate, and perhaps most importantly, what things you're going to like. But has Web 3.0 arrived in its full semantic glory? No, not yet. But it's clear we are getting closer than ever before.

To begin, there's the seemingly minor announcement from Xmarks, the company formerly known as Foxmarks, but now rebranded thanks to their multi-browser support. Xmarks has introduced additional features to their bookmark synchronization product which include things like site suggestions and smarter search. By leveraging their large stash of data (600 million bookmarks), Xmarks is now able to recommend sites right within your search results. This is done by placing an Xmarks icon next to those results which are most popular, meaning most bookmarked, on their service. Also, when you visit a web site and click the Xmarks icon in your address bar, Xmarks will return a list of sites similar to the one you're currently browsing.

The data used to deliver these recommendations and suggestions are anonymized - a good thing considering that our browser bookmarks are often the ones we have specifically chosen not to share with others. For bookmarks to become recommended in this fashion, they must be fairly popular on the service - a level that's determined by the number of times saved as a percentage within a particular category.

In a way, what Xmarks is doing is very similar to what StumbleUpon's browser extension does too. Like Stumble, Xmarks annotates our search results highlighting those that may be of value to us. Yet Xmarks takes it a step further by discovering related sites, too.

The Smarter Tracking Tool (Evri)

Another company revealing new innovations here at DEMO 09 is Evri, a semantic search engine which understands what's called "natural language." Evri knows the different parts of a sentence (subject, verb, object) and it knows how those parts are connected to each other.

Although still too raw to be your main search engine, Evri has a new "Collections" feature which lets you follow topics (aka search queries) that are of interest to you. After returning a list of search results which include Wikipedia entries, news articles, videos, and images, you can click the star labeled "Follow this" to continue to track that topic. What's missing from this feature, though, is an alerting system which will inform you of updates via email or RSS. However, the company says that's coming later on.

Evri is also branching out from being a web destination alone by introducing Evri widgets which can now be seen in action on the Washington Post's web site. These widgets parse the content on the page to deliver smart recommendations of similar articles both on the site itself as well as elsewhere on the web. 

Another new feature launching now is Evri's browser toolbar. By clicking on a button next to the Evri search box in the toolbar, the people, places, and things on a web page are highlighted. Click on these items and pop-ups appear with more information about the keyword, what's related to the topic plus news, images, and videos.

This additional layer of information on top of standard text makes browsing the web and reading articles a deeper and richer experience. No longer do you need to perform web searches in a separate window to understand definitions, context, and meaning. Instead, Evri's toolbar adds an intelligence to the web that was never there before. It's clear that the company is still working towards making that additional layer more accurate and more relevant, though, but conceptually the idea is solid.

The RSS Reader That Learns (Ensembli)

Ensembli, an RSS reader of sorts, takes a different approach to tracking topics than Evri does with its "Collections" feature. Where Evri's UI can sometimes feel a bit cluttered with its multimedia results, Ensembli's interface is simple - you just type in a topic and it will continue searching for new articles related to what you entered. But this reader doesn't simply pull information for you - it learns what you like. Every time you read, ignore, or discard a story, Ensembli gets to know your tastes a  little bit better.

While this feed reader is far too simplified for RSS junkies like us, it's easy to see how Ensembli could be a good introductory tool for RSS beginners. Still, the sources it returns sometimes seem lacking and it's hard to say if this will ever be any more useful that a simple Google Alert, for example. Nevertheless, it's not really the feed reading itself which makes Ensembli intriguing, it's the learning element. Whatever algorithm is at work behind the scenes figuring out your likes and dislikes is what's the most important aspect of this new technology.

Getting Smarter...Little by Little

Taken by themselves, the above announcements may have seemed more evolutionary than revolutionary, but look at them within a broader scope and you can see a pattern beginning to develop. In this transitional period from Web 2.0 to Web 3.0, we're starting to see tools and services that aim to expand upon the traditional search experience in order to deliver us to a more intelligent web. On this new web, we're moving beyond SEO and PageRank to determine relevance and instead are seeing new technologies develop that better understand meaning, context, and personal preferences.

Stayed tuned...part 2 of "The Smarter Web" will continue tomorrow.