Archive for July, 2007

O Streamy, Where Art Thou?

StreamyMy traffic stats tell me that streamy.com refers a few readers to my blog every so often. I think it’s great that a few people are reading my blog from Streamy. When do I get an invitation and the accompanying ability to read my blog and others there? I really want to know to what extent it’s a network of readers and a network of the people and the issues in the news.

I’m hopeful for the former but will be surprised if Streamy’s “filters” can master the latter.

Applying the Three Components of Networked News

For a goodly time now, expect to see this blog try to flesh out this concept of networked news by reviewing isites that concern themselves with the news, either completely or substantially—from the New York Times to digg to Google Reader to Mario Romero’s awesome Google Reader Shared Items app for Facebook to Topix to Memeorandum to Pageflakes to Thoof to Streamy to whatever else with which I may cross paths.

I’ve got my three metrics, and it’s time to make sense of them. Maybe along the way we’ll figure out what some bright person could do to satisfy Arrington’s appropriately underwhelmed feeling about online news. Maybe we’ll figure out that “networked journalism” has something to do with networked news. Maybe not.

But what’s first?

Google Reader Shared Items: Questions

What’s the scene here? Why the seeming discrepancy between these two screenshots, captured at the same time?

google reader shared items screenshot

Above, Hal Espen’s page of shared items: Espen seems to have shared the Network(ed)News post called “What Is Networked News?”

google reader shared items screenshot

Above, my page of shared items: Aside from my own share, only Mario seems to have shared the Network(ed)News post called “What Is Networked News?” Wait, where’s Espen?

What happens when someone shares an item already shared? Is shared ‘original’ item x the same thing as shared ‘shared’ item x?

Consider the following case: I post to Network(ed)News and someone like Mario gets my feed and shares it such that his facebook application registers that act of sharing and tells me as much, as it does above. Did Espen maybe see my post because he subscribes to Mario’s shared feed and then share the post himself? It doesn’t look like it, since the “via:” attribute says “Network(ed)News,” not something like “mario’s shared items in Google Reader.” It looks like Espen just subscribes to my feed, so why no aggregation? The post is relatively new, so I don’t think the issue is one of time—in which, for instance, Mario shared the item fewer than twenty-four hours ago and Espen more. But who knows really.

Haha. I need all the aggregating help I can get.

What Is Networked News?

Networked news describes a structure for consuming information. It means pulling in your news from a network of publishers—bloggers and traditional news outlets. It means pulling in your news from a network of readers—friends and experts and so on. And, crucially, networked news means breaking down the bits of content into their relevant constitutive pieces and reforming those pieces back into their own network. It means pulling in your news from a data-driven network of the people and the issues in the newspeople like George W. Bush and Steve Jobs and Oprah and issues and memes from “republican” and “iraq war” and “campaign 2008″ to “iPhone” to “power of forgiveness.”

The concept of networked news grows out of the realization that the stories we care about exist between one author and another, between articles and blog posts, between newspapers and blogs. The story is a kind of thread that runs through time and in and out of the person-subjects and issue-topics of the news.

Networked news is not networked journalism, which is a structure for publishing information. See pressthink, buzzmachine, and newassignment.net for that parallel “genius” project to grow and diversify the number of sources from which we pull our news.

NewsmapThe first and second components of networked news are new but not unprecedented. Pulling in your news from a network of publishers is what we do when we subscribe to RSS feeds and read them in one place. It’s the river of news I read when I fire up Google Reader, which gives me news about the tech industry, about finance, and about politics. Techmeme, Memeorandum, Google News, and other memetrackers are other great examples of networking news from publishers. Newsmap, based on Google News, is the picture of this first component. Thoof and other news-focused web apps with similar recommendation engines also represent this publisher-based side of networked news.

Pulling in your news from a network of other readers is what Mario Romero is working on with his Google Reader Shared Items application for facebook. It’s also what Digg and others represent.

There are sites that represent both the first and second components of networked news. It’s what Newsvine, Topix, Daylife, and others represent. It’s what Pageflakes, Netvibes, iGoogle and others represent. Though I haven’t actually toyed with the site yet (I’m still waiting on that invite, guys) it looks like Streamy sits at the current bleeding edge of the reader-based front of networked news.

The third component of networked news is, in some ways, the oldest, represented by simple searches to Google News or Technorati tags. It’s also the most difficult component—technically, socially, you name it. When I encourage Mario to let users browse his Google Reader Shared Items by tag, I’m encouraging him to let us readers of news pull in bits of content by issue and meme. When Streamy claims to have “filters”—which I called “substance- and source-based ways browse, and subscribe to, kinds of content, by keyword and original author, respectively”—it’s claiming to have taken a few steps into the this elusive third component of networked news.

networkOne kind representation of this third component, in the form of how Exxon putatively buys scientific research, is graphic. The “story” is the whole visual network, while the actors are broken down and interconnected within it. The bits of content, in this case, come in the form of profiles on each actor pictured. People and foundations are linked up by bridges connecting them. Those bridges, exxonsecrets says, represent the money that Exxon funnels through the foundations to pay the people to conduct and promote bogus climate research. Users can create, manipulate, and save their own graphical network maps for all to see.

A swirl of excited ideas in my head, it’s all rather tough to articulate. But I’ll get to it soon enough, bit by bit.

Aggregate Them This Way, That Way, Your Way, My Way

People are into this new facebook application called Google Reader Shared Items, developed by a nice guy named Mario Romero. That’s natural, because the application takes a couple steps down the path toward truly networked news.

Denise Howell loves how it lets her discover the feeds of people with whom she shares facebook group memberships. The app lets her check out individuals’ lists of shared items or grab the url for their shared items’ respective feeds and pull them into Google Reader. Scoble agrees: “This app has already helped me find some great new feeds. It’s interesting to see what you all are reading and sharing through Google Reader.”

All that’s great for what it’s worth, but the Shared Items application’s ability to aggregate is where the real gold is buried. When Mario can fully dig up that code, we’ll behold something of a real treasure. I’ve written before, in response to a thoughtful post by Jeff Jarvis, about how I think “the article”—or, more generally, bits of content like blog posts, newspaper articles, podcasts, etc.—”has taken the story hostage.” Bits of content strike me as an inelegant medium for the news, even if they seem roughly economically necessary. Aggregating those bits of content and then paying attention only to the popular ones that float to the top of the stack helps us move toward the story.

This is a tricky point—one I hope I can tease out here. When we look at the list of “Top Shared Items,” we’re looking at something more than popular newspaper articles or blog posts. We’re looking at something greater. We’re looking at bits of content that have grown into stories. This isn’t digg, where from the beginning we would vote because we wanted to hop on the accelerating train just because we hoped it was going someplace. These votes are, at least at this early point, done in private. Maybe others play it like a game, but I share an item because it’s equivalent to starring it and saving it for later and because that item gets sucked into my blog’s sidebar. (Well, I also share all my own posts just because they’re one share more likely to make the list of Top Shared Items.)

These bits of content grow into stories because we’re all reading them. They undergo a process of sublimation—from a private unit of discrete information to a public unit of shared importance. They go from data to meme.

Descending from the clouds, a list of the ways I’d love to slice up facebook’s shared items from google reader:

  • by most shares universally
  • by most shares within facebook networks
  • by most shares within facebook groups
  • by most shares among people I select ad hoc
  • by tag, as I pointed out to Mario on his good facebook group
  • by tag AND by most shares within networks, groups, or my ad hoc selections

Mario’s already built the first and the second. What his application offers with respect to groups is access to the shared items of each member. For small groups especially, the data must be very scarce, and so pulling the top shared items from groups is less important to me. What I’m really axed for, however, is my own ability to choose which people’s shares I aggregate. And then I want to focus on certain topics, like “facebook” or “news” or “iraq” or “alberto gonzales,” within those aggregations.

Also, Google Reader is still a better place for me to read posts. I’d like it to be able to pull in a feed of the various Top Shared Items. I’d love to subscribe to a feed that comprises posts that receive, say, ten shares in twenty-four hours. Note the deep curiosity here. Different instances of Google Reader pull in publishers’ feeds. Those instances of Google Reader produce different feeds of their own; publishers’ feeds have become readers’ feeds. Facebook then pulls in those readers’ feeds, aggregates them, and displays those aggregations. If facebook could turn those aggregations into a feed, I’d read them back in Google Reader. Then readers’ feeds have become Readers Feed.

Thus each post would have traveled an odd, inspiring, and transformational course: Google Reader to facebook and back to Google Reader. And that, at long last, is why facebook has astonished us all as a platform. Yes, facebook as platform can help us network our news. I wouldn’t have dreamed of that when I signed up for facebook in 2004.

Also also, I’m sure Mario’s thought about this, but it would be super nice not to have to navigate away from my facebook profile page in order to view other lists.

Also also also, @mario, in Google Reader my posts for Network(ed)News are full-length, but in facebook, they’re truncated. How can I keep them full-length in facebook?

Google Reader Shared Items Needs Fewer Friends

I have only one “friend” who has the application. That’s Robert Scoble, and I’d rather browse his link blog in Google Reader itself. There’s no reason for me to check it out in facebook.

There’s a mismatch between a facebook friend, who’s someone I usually know personally and often care about a great deal, and someone whom I’d like to include in the limited group of people whose Google Reader preferences I care about.

I’d love it if lots of my favorite bloggers kept “shared” their favorite posts and brought all that into facebook. I’d love to have Jeff Jarvis’s favorite reads. I’d love to have Doc Searls’s and Dave Winer’s. Yada yada.

But I’m not sure I want them to be my facebook friends. I don’t know them, haven’t met them. Equally as true, if not more, is that they are unlikely to want me to be their facebook friend.

The Google Reader Shared Items application should move away from the conception of “friend” native to facebook. Call the new conception a “follower,” and don’t allow the followed any choice, once they’ve hit shift+s in Google Reader, about whether I snoop in on what they’re reading. After all, I don’t have to know, or even like, Scoble to pull the feed for his link blog into my Google Reader.

I want to use this facebook app to actively subscribe to many individual’s shared items feeds. That’s because, in the end, there’s really only one important feature the app needs: aggregation how I want to aggregate.

Maybe that’s by tag. Maybe that’s by my favorite tech bloggers. Maybe the time comes when I can pull together the recommended reads from my favorite dozen political blogs—Think Progress, Matt Yglesias, Josh Marshal, Kevin Drum, Scott Horton, and others. Maybe I want to aggregate by my best friends forever. It should be up to me.

Spivack Gets the Semantic Web, But the Analogy Eludes

Nova SpivackI simultaneously envy and fret over Nova Spivack’s style. I’m deeply sympathetic to his recent brain metaphor—in no small part because I’m a sucker for the killer analogy. Spivack’s analogy is catchy and seems useful: “I believe that collective intelligence primarily comes from connections—this is certainly the case in the brain where the number of connections between neurons far outnumbers the number of neurons; certainly there is more ‘intelligence’ encoded in the brain’s connections than in the neurons alone.” Then, bringing it home, “Connection technology…is analogous to upgrading the dendrites in the human brain; it could be a catalyst for new levels of computation and intelligence to emerge.” Ultimately, Spivack claims, “By enriching the connections within the Web, the entire Web may become smarter.”

There’s great stuff packed in here—frustratingly great stuff. Is there really more “intelligence” encoded in the brain’s connections than its neurons? What does it mean to believe that collective intelligence comes from connections? Or are we talking tautology (in which “intelligence” + “connections” = “connected” or “collected” or “collective intelligence”)? And what could it ever mean to upgrade, or enrich, our dendrites, the byzantine tree-like conductors of electrical inputs to our neurons? How would we be more intelligent?

Why not rehearse an argument that defends the aptness of this analogy? Why leave that chore—the really hard part—to me, to the reader? Unless they’re trivial or obvious, rigorous analogies alone cannot be more than invitations to real arguments. Don’t invite me to the party and tell me to bring the champagne!

“The important point for this article,” Spivack writes, “is that in this data model rather than there being just a single type of connection”—the present Web’s A-to-B hotlink—”the Semantic Web enables an infinite range of arbitrarily defined connections to be used.” Bits of information, people, and applications “can now be linked with specific kinds of links that have very particular and unambiguous meaning and logical implications. … Connections can carry more meaning, on their own. It’s a new place to put meaning in fact—you can put meaning between things to express their relationships.”

Yes, when connections can carry arbitrarily more meaning, the human-relevant reasons for them to exist grow arbitrarily large—or, at least, as arbitrarily large as we bandwidth-bounded humans can handle. Only this kind of virtuous semantic circle, it seems to me, can radically improve the intelligence of the web as whole. What’s important are not just connections with more meaning (“upgraded” dendrites, I suppose). What’s important is that connections with more meaning promise a blossoming of the total number of connections (more “dendrites”)—each of which can themselves have more meaning.

The web will become more intelligent, or just more useful, when projects like Spivack’s and like Freebase—which I’ve checked out a bit (facebook me for an invitation to the private alpha)—expand the scope of reasons for connections among bits of information, people, and applications. Of course, that’s the whole idea for the semantic web. With more reasons for connections, we get more meaning for connections. With more meaning for connections, we get more connections. In the end, we get more connections with more meaning—a kind of semantic multiplier effect.

It’s just that we’re talking about Internet here. Brains are still a few years out.

Love Scoble’s Facebook Yammering!

Robert ScobleI love it so much that I think I’ll add more. But let’s be kinder to ourselves—for good reason!—and not call it yammering. Blabbing? No, that won’t do either.

How about exegesis-ing? Ya, that’s perfectly highfalutin. Facebook is serious stuff, man.

And so I come to point out that Facebook should allow each of us to dismiss items in our News Feeds. Then they could learn what don’t like. In turn, that would free up lots of valuable real estate for news items we do like. The result is a personalized News Feed we all appreciate a little more.

But what to do about ads? Does it make sense to allow us to dismiss those as well? Will miserly users like me automatically dismiss all ads just to spite the advertiser? Maybe, but other users may dismiss only ones they really don’t like and make room for Facebook to serve up ads they do. Being able to separate the wheat and chaffe, of course, allows one to reap more value from the wheat.

While we’re on the topic of Facebook, meanwhile, I’d also love it if I could browse the items on the Google Reader Shared Items app by tag. I would click on a tag in the feed and then see the most popular posts, among my friends or universally. That would be one small step toward a new world of news in which the bits of content that discuss people and issues are actually contingent on those people and issues (well, in this case, issues, anyhow). That would be one small step toward letting the “story” wiggle free from the “article” or “post”—the “bit of content,” as it were. (I posted my request on Mario Romero’s dedicated “request features” discussion board.)

UPDATE: Mario responds to my request, which I called “pie-in-the-sky” on Facebook: “Josh: Thanks for the tip that is DEFINITELY on the to-do list!” Nice!

Ornery Arrington on Personalized News

streamyI can’t get enough of how Michael Arrington, at TechCrunch, rags on personalized new sites. I love it. Talking about the newest venture to join the hunt for a killer solution to online news, Arrington writes of Streamy, “It’s pretty and extremely well thought-out, but it’s not clear that it does anything new enough to grab people’s attention.” Plus, “It is well designed, has lots of intelligent features, and is almost sure to drop into obscurity immediately after launch.” What a guy!

For now, I’ll withhold real judgment—especially of the claims made here—till I’ve had a chance to check out the private beta, to which I’ve requested an invitation.

Meantime, I’m impressed with the social networking and what they call “filters,” which are essentially substance- and source-based ways browse, and subscribe to, kinds of content, by keyword and original author, respectively. Ideally, that would mean that someone could set up his personalized page to pull in everything Arrington says about, say, “personalized news.” The trick, then, is how social networking brings the virus to the filter. Your friend’s becomes your filter and then it becomes my filter; my filter becomes yours, your friend’s, his friend’s, and so on.

Note that amateurs don’t have to control these filters either. Arrington could promote his own, using them to help him slice up his content along different lines, potentially overlapping lines, and push it out Streamy readers of news, if there ever are any. Or these filters could allow someone to become an editor, much the way Scoble acts as an editor (choosing the news) with what he calls his link blog. If I were running Streamy, I’d be thinking about how I could allow users to monetize their filters. They are, after all, just platforms for subscriptions. Find a way to allow an expert on some tricky topic like global warming sell you his daily digest of the best reading on climate change. He could write his own original material as well, of course, and include it in his filter.

I agree with Arrington that integrating IM is smart, and integrating the ability to drag and drop stories is super smart. I wonder how popular it will become for sites to integrate IM. Will the New York Times have it some day? Will espn.com? It seems to make sense to distribute IM over virtual locations rather than keep it all cooped up in one place. If I were running meebo, I’d be thinking about how I could build a proprietary web-based IM client just for the New York Times. (From what I can tell, Jake Jarvis has tried to turn meebo into a Facebook app. I signed up, but got an error: “There are still a few kinks Facebook and the makers of Meebo are trying to iron out….”) I love meebo almost as much as Arrington’s orneriness.

Here’s the screencast of Streamy:

Grokky Jarvis Has Something to Say about the News

Jeff JarvisJarvis is correct. Equally important in the land of new ideas, however, is vivid articulation. Write the truth, and write it well.

And so he does. To wit, “Like most everyone else chasing this golden fleece, I’ve defined [hyperlocal news] as content, news, a product, listings, data, software, sites, ads. It’s not. Local is people: who knows what, who knows whom, who’s doing what (and, yes, who’s doing whom). The question should be…how we bring them elegant organization.” That’s Zuckerberg’s term—elegant organization. Jarvis likes it a lot. He’ll tell you about it too.

“I now believe that he who figures out how to help people organize themselves,” Jarvis continues, “letting them connect with one other and what they all know, will end up with news, listings, reviews, data, gossip, and more as byproducts.”

I’ll take it from here. The generally news-based web application must organize its information around functional units that are most relevant to the subject. When the subject is news, the most relevant functional units are people and and issues and organizations. Note that a full thirty percent of google searches are for people, for instance, says Jaideep Singh, CEO of Spock in this July 2 PodTech video. Also note the proliferation of person-centric search engines, like Spock, ZabaSearch, Pipl, PeekYou, and Ligit, to name a quick few.

Today, however, the news is still fundamentally organized around its content, its tiny bits of content, its data, whether those be newspaper articles, blog posts, podcasts, or webpages. That organization—in which people and issues are contingent upon the bits of content that discuss them—is a relic of paper and, just as important, html. The article has taken the story hostage. That must be turned on its head: the bits of content must be contingent on the people they discuss. The people, and also the issues, who constitute the story, as it were, must be liberated from the confines of the article. That’s the promise the internet makes to journalism in the twenty-first century. That’s the promise the database makes to news.

A newspaper article will get broken into pieces, like legos that interlock: “little objects,” as Scoble once called them. Those objects will be stored individually, deployable individually, graphable individually. Individually, but not alone. They will live in cells among millions of others cells, part of semantic hive buzzing with the fervor of the world’s news. Or at least the world’s news according the internet.

By slicing up the data, by breaking up the data, we can put it back together. Only we can put it back together however we like, as individuals and as a collective—confident in our ability to tell whatever story may yet be lurking in the interstices of modern journalism. Blogs created an army of journalists. The web needs an application that will arm a legion of editors, each driven largely by their own individual tastes for consuming news but cooking up social feast of intelligent information.

Jarvis again: “People, not content. People, not data. People, not software.” Wait, not software? Getting a bit carried away is a small price to pay for generating so much momentum in the first place.


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