Archive for the 'Thoof' Category

Another boring personalized news service

I love seeing more and more copycat “intelligent” personalized news sites. The good news is that means that there are funders out there who still know in their gut that there’s money to be made on innovation in the news business. They just need the one idea that will stick. And go pop.

Meantime, more than a six months ago, Mike Arrington wrote about a site called Thoof. Back then, I was also writing and thinking about Streamy and FeedEachOther and other unmemorable twists on feed readers and personalized news sites. No matter their differences, they all seem the same. I just came across yet another—Tiinker—and I just can’t bear it any more.

In his write-up of Thoof, Arrington frames the debate as taking place between two competing positions. He believes that “the masses want popular news,” while the Thoof’s CEO believes that “the masses want tailored news.”

I think they’re both wrong and come at the issue the wrong way.

People want their news based on others’ interests—specialized news from friends (those who have similar interests) and widely popular news from the masses (everyone else). And they want their news based on their own interests, even if their friends don’t share those interests.

Now suppose there’s a continuum of users—from RegularJoe on one end to PowerUser on the other.

RegularJoe wants his news from other people. Although he has relatively few “friends” online, and is thinly connected to the ones he has, he wants them to put in most of the effort to help him get specialized news. (He likes read the “Most Emailed” news articles but doesn’t email them, or he likes visiting Digg but doesn’t log in and vote.) RegularJoe is mostly interested in widely popular news.

PowerUser is different and wants his news mostly based on his own interests. But it would be a mistake to think that he pursues his interests alone (no man is an island, says Donne). He has relatively many friends and enjoys pushing and pulling mutually interesting news to and from them. Of course, PowerUser also has news interests that his friends don’t share or don’t share as strongly, and so he pursue his news independently from his friends as well. Because he enjoys consuming a lot of information, moreover, PowerUser is also interested in widely popular news (he wants to keep his finger to the pulse).

These purely black-box algorithmic personalized news sites don’t really fit either guy.

RegularJoe: They’re too hardcore for RegularJoe. He doesn’t want his own news because his interests just aren’t sufficiently deeply cultivated. RegularJoe isn’t motivated enough to build up a profile by clicking “thumbs up” all the time (as tiinker would have him). When he is motivated enough, he isn’t sufficiently consistent over time for these fancy algorithms to get him what he wants before he strays back to cnn.com because it’s easier to let someone else decide (a person-editor, in this case).

PowerUser: They’re too secret for PowerUser. He wants to put in more effort cultivating his interests and doesn’t want to trust an (anti-social) algorithm from some start-up that might disappear tomorrow. PowerUser also wants to get specialized news from niche groups of friends. For him, the fact that friends X, Y, and Z read some blog post makes it inherently more interesting because they can have a conversation about it (broadly speaking). The personalized news sites just aren’t sufficiently social for the PowerUser who wants to interact with friends around the news.

This isn’t meant to be a slam-dunk argument. I’m not sure about what happens with the group of users who are in the hypothetical middle of the continuum. Maybe there’s some number of users (1) who care enough about the news to have non-trivial interests that don’t shift or fade over time but (2) who also don’t care very much for a transparent or social experience of the news. Ultimately, however, I really doubt that this group of users is big enough to support this kind of personalized news site.

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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?

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.

Thoof Is Close But Not Nearly Close Enough

ThoofI think recently launched online news site thoof has taken another step in the right direction—toward giving its readers the news they want and withholding the news they don’t. Thoof seems to learn what its readers like buy remembering their clicks on article summaries that given user-assigned and -edited tags. If you click on an article summary with the tag “politics,” the next time you visit you’re more likely to see article summaries tagged “politics.”

But Mike Arrington’s concern, inspired the fact that “web is littered with failed or stagnant personalized news startups,” is still relevant. People do “usually want to read news and then discuss it with friends. That is why people still tend to prefer big news sites: “everyone else flocks there, too.”

If it wanted to, however, Thoof could keep something like main page that displayed the aggregate of all its readers’ preferences. Each reader could then, at any time, choose whether to read his personal page or the page that reflects the many, many decisions of the Thoof community. Then Thoof might be able to satisfy Arrington’s worry that “the niche audiences that really want personalized news aren’t enough to sustain these startups.” Thoof can have it both ways.

Better, in fact. Readers who want the news the “flock” sees get a much purer version of it, an elevated version of that. The flock isn’t just reading the same thing—they’re causing themselves to read the same thing along the way. They’re not just readers, led by a shepherd. They’re editors, and they lead themselves.


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