Archive for the 'Read/Write Web' Category

Open Salon on CJR

After none too few rounds of editing—which is decidedly not to say they weren’t thoroughly helpful—my piece about Salon.com’s newest adventure in blogging is up for your leisurely weekend enjoyment at the Columbia Journalism Review. Hot dog!

Thanks go to my friend and editor. He’s the kind of guy who works really hard, gets tired because of all that work, loves to sleep because of how tired he is, but is called indefatigable anyhow. Justin Peters, everyone! You may know him from such happy locales as diaperville, USA.

Whither Tag Clouds?

A few weeks ago, one could do relatively little clicking around the interwebs and notice the tear of pretty tag clouds powered by wordle. Bloggers of all stripes posted a wordle of their blog. Some, like Jeff Jarvis, mused about how the visualizations represent “another way way to see hot topics and another path to them.”

For as long as tag clouds have been a feature of the web, they’ve also been an object of futurist optimism, kindling images of Edward Tufte and notions that if someone could just unlock all those dense far-flung pages of information, just present them correctly, illumed, people everywhere would nod and understand. Their eyes would grow bright, and they would smile at the sheer sense it all makes. The headiness of a folksonomy is sweet for an information junkie.

It’s in that vein that ReadWriteWeb mythologizes the tag cloud as “buffalo on the pre-Columbian plains of North America.” A reader willing to cock his head and squint hard enough at the image of tag clouds “roaming the social web” as “huge, thundering herds of keywords of all shades and sizes” realizes that the Rob Cottingham would have us believe that tag clouds were graceful and defenseless beasts—and also now on the verge of extinction. He’s more or less correct.

I used to mythologize the tag cloud, but let’s be honest. They were never actually useful. You could never drag and drop one word in a tag cloud onto another to get the intersection or union of pages with those two tags. You could never really use a tag cloud to subscribe to RSS feeds of only the posts with a given set of tags.

A tag also never told you whether J.P. Morgan was a person or a bank. A tag cloud on a blog was never dynamic, never interactive. The tag cloud on one person’s blog never talked to the tag cloud on anyone else’s. I could never click on one tag and watch the cloud reform and show me only related tags, all re-sized and -colored to indicate their frequency or importance only in the part of the corpus in which the tag I clicked on is relevant.

But there’re also a cool-headed thoughts to have here. If tag clouds don’t work, what will? What is the best way to navigate around those groups of relatively many words called articles or posts? In the comments to Jarvis’s post, I asked a set of questions:

How will we know when we meet a visualization of the news that’s actually really useful? Can some visualization of the news lay not just another path to the “hot topic” but a better one? Or will headlines make a successful transition from the analog past of news to its digital future as the standard way we find what we want to read?

I believe the gut-level interest in tag clouds comes in part from the sense that headlines aren’t the best way to navigate around groups of articles much bigger than the number in a newspaper. There’s a real pain point there: scanning headlines doesn’t scale. Abstracting away from them, however, and focusing on topics and newsmakers in order to find what’s best to read or watch just might work.

I think there’s a very substantial market for a smarter tag cloud. They might look very different from what we’ve seen, but they will let us see at a glance lots of information and help us get to the best stuff faster. After all, the articles we want to read, the videos we want to watch, and the conversations we want to have around them are what’s actually important.

Sell me tags, Twine!

How much would, say, the New York Times have to pay to have the entirety of its newspaper analyzed and annotated every day?

The question is not hypothetical.

The librarians could go home, and fancy machine learning and natural language processing could step in and start extracting entities and tagging content. Hi, did you know Bill Clinton is William Jefferson Clinton but not Senator Clinton?! Hey there, eh, did you know that Harlem is in New York City?! Oh, ya, did you know that Republicans and Democrats are politicians, who are the silly people running around playing something called politics?!

Twine could tell you all that. Well, they say they can, but they won’t invite me to their private party! And maybe the librarians wouldn’t have to go home. Maybe they could monitor (weave?) the Twine and help it out when it falls down (frays?).

I want to buy Twine’s smarts, its fun tags. I’d pay a heckuva lot for really precociously smart annotation! They say, after all, that it will be an open platfrom from which we can all export our data. Just, please, bloat out all my content with as much metadata as you can smartly muster! Por favor, sir! You are my tagging engine—now get running!

What if Twine could tag all the news that’s fit to read? It would be a fun newspaper. Maybe I’d subscribe to all the little bits of content tagged both “Barack Obama” and “president.” Or maybe I’d subscribe to all the local blog posts and newspaper articles and videos tagged “Harlem” and “restaurant”—but only if those bits of content were already enjoyed by one of my two hundred closest friends in the world.

I’d need a really smart and intuitive interface to make sense of this new way of approaching the news. Some online form of newsprint just wouldn’t cut it. I’d need a news graph, for sure.

See TechCrunch’s write-up, Read/Write Web’s, and Nick Carr’s too.

PS. Or I’ll just build my own tagging engine. It’ll probably be better because I can specifically build it to reflect the nature of news.

If you got excited about Streamy…

…then you should check out FeedEachOther. That’s what Marshall Kilpatrick of R/WW says. If you were let down with Streamy, on the other hand, it looks like you will also be let down by FeedEachOther.

What’s the bummer? These “feature-rich super-social RSS readers” just aren’t that feature-rich or social. They’re just not so different from Google Reader. They’re still RSS readers.

But first the good news. The thing pulls comments from the original blog into the reader. That’s awesome. Multiple kinds of relationship are good too.

I don’t want to subscribe to “similar” feeds according to some recommendation that’s a huge black box. In fact, it doesn’t really even work, and its black-boxiness prevents me from knowing why. Why, for instance, does FeedEachOther only give me recommendation based on the whole feed? Why not on each post? Whole feeds contains posts way too diverse to derive sufficiently sufficient semantic patterns from them.

It’s not okay to look at all of Jeff Jarvis’s feed and offer me this string of banal tags: “advertizing – buzz – internet – news – technology – blogs – daily – marketing – politics – web – blog – commentary – jarvis – online – trends – business – imported – media – tech – web2.0 – blogging – culture – journalism – opinion – tv.” Setting aside the problem of blogs-blog-blogging, it’s not okay because they’re so generic and because I can’t stack them up and take their intersections. I can’s use these tags the way the people who created them use them. When someone in delicious tags something “journalism,” they might also tag it “trends.” Neither topic is interesting alone; only together are they interesting. (Indeed, ‘trends in journalism’ is very interesting.)

Plus! On top of reading each post’s comments with a feed, I can share notes and items within the system. But wait! “The only thing better” would be to post comments from the web app to the original post? Actually, that’s a lot better. That’s worlds and worlds better. A web app is still just a basic RSS reader until it can weave itself into the same cloth of which the many, many thousands of blogs with their comments are made.

So, no, “the absence of offline and mobile modes, weaker analytics than Google Reader offers and a limit of 500 feeds by OPML import” are not the “only shortcomings.” Someone’s seriously drinking the RSS Reader Kool-Aid. And that’s too bad—because RSS itself is so many times greater and more magnificent.

In the end, Google Reader, Streamy, and FeedEachOther are bastions of only ONE component of networked news. They allow readers to network the news by publisher. Sure, they do more than dabble in allowing readers to network by fellow readers. There’s got to be more though—comments from reader to blog would be a big step. Lastly, both Streamy and FeedEachOther just don’t have the necessary kind of semantic (or “Semantic”) insight into their content yet. The three components of networked news must be as one for any to be truly worthwhile.

When will my news platform serve me up content that’s from my favorite author and recommended by my good buddy and about my favorite subject or story or beat? When that happens, we’ll not only all be reading our own really interesting stuff—we’ll care enough about it to get into even more interesting conversations.

News Graph?

Mark Zuckerberg once upon a time extolled facebook and told us about this thing called a “social graph.” Bernard Lunn has just talked about an “innovation graph.”

What about a “news graph”? Hubs and spokes—call them nodes and bridges.

Nodes are the people who are the subjects of the news. Like Karl Rove or Paris Hilton or Chuck Prince. Maybe nodes can also be groups of people acting as a single agent. Like the 100th Congress or the Supreme Court or maybe even something really big like Disney Corp.

Bridges are the news issues connecting the people to whom they are relevant. Here, the bridges have substance apart from mere connection. It would be like a social graph having connections indicating different kinds of friendship—a solid line for a great friend maybe, and a dashd line for a business acquaintance. Think of bridges like tags, just like those you might in delicious. You find a piece of news, which comes in the form of a newspaper article or a blog post, for example, and you assign issue-tags to it. Then, in turn, you assign that article or post to the people-nodes whom it discusses. The issue-tags flow through the article to the people-nodes to which the article or post is assigned; the pieces of news fall out of this picture of the news graph.

When people-nodes have issue-tags thus associated with them, we can indicate when certain people-nodes share certain issue-tags. If we represent those shared characteristics with bridges that connect the people-nodes, we’re graphing the people in the news and the substantive issues that bind them all up into the story of the world at some slice in time.

Just note once more how the pieces of the new—the bits of content, as I call them—fall away and liberate the news and the people and issues it comprises from the narrow confines created by the printing press and furthered by HTML. (Check out Jarvis’s more than mildly inspiring post.) This kind of news graph would, at long last, make the bits of content contigent on the people and the issues they discuss. It’s the elegant organization for news.

This is, by the way, the third component of networked news. This is the data-driven network of the people and the issues in the news.


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