Man vs. Machine: Can Software Trump Editors in the Newsfeed Wars?

Xconomy National — 

Suddenly, digital newsfeeds are the hottest thing in consumer tech.

Big tech companies have been frantically opening their wallets in recent weeks to buy out small startups making mobile-friendly applications that use software to pull together news from innumerable sources around the Web.

Last week, it was Google paying a reported $30 million or more for Wavii, a Seattle-based startup that used language recognition and processing software to distill information from online news reports.

Two weeks earlier, professional networking website LinkedIn confirmed it was buying top-ranked newsfeed app Pulse for some $90 million.

A month ago, Yahoo got a flood of headlines for cutting its own $30 million check for Summly, another app that sliced news articles into a more mobile-friendly format—or, as Summly put it, “algorithmically generated summaries from hundreds of sources.”

This latest flurry of transactions follows CNN’s 2011 purchase of the app Zite, which pulls together news feeds for readers in a stylish, digital magazine-like layout.

What’s going on here? Think of it as a natural outgrowth of the social media age, which got a lot more people comfortable with ranking and rating the things they like, places they visit, and information sources they trust.

While Google, Yahoo, and LinkedIn haven’t been able to compete with Facebook’s ability to build a “social graph,” those companies are all clearly hoping that a user’s ability to personalize their own information and news feeds will give the providers deep insight into each consumer’s interests and preferences.

That kind of data, in an age when we’re all going to start generating more digital crumb trails with each passing year, will probably be a goldmine for advertisers—and the companies who can sell to them.

But it does set up an interesting question about quality. Right now, algorithm- and artificial intelligence-based apps that comb over disparate sources of information, pull out the most important parts, and reassemble them in a smaller package can do a good job of telling a user what’s going on in the broader news world.

So, for example, an algorithmic newsfeed might crawl the Web and see a lot of reports about the big college stars chosen in the first round of the NFL draft. It would spit out to the user a short, concise sentence: Kansas City Chiefs select Central Michigan offensive tackle Eric Fisher with the first pick in the draft.

When it works, it’s pretty cool. But in my experience, so far, the human-powered versions do it better—especially in critical situations.

Breaking News, a unit of NBC, employs a team of editors who pull together reports from a variety of sources into a “real-time feed that focuses on just what’s new.”

Breaking News, a unit of NBC, employs a team of editors who pull together reports from a variety of sources into a “real-time feed that focuses on just what’s new.”

Wavii is the example I’m most familiar with as a user. The ideas behind the app were very cool, and the technology they were employing was lauded as ahead of the curve and very smart.

The ambition was certainly aimed in the right direction—when it launched publicly about a year ago, CEO Adrian Aoun told me that “it makes you wonder why something like what we’re building doesn’t live inside Google Plus.”

But over several rounds of trying Wavii on my smartphone, I was never quite able to get the stuff I actually wanted to see consistently on my main feed. There was too much tech news and celebrity stuff for my reading interests, which are more general and local. The sources it linked to also were frequently some kind of off-brand website that was aggregating other news.

There is an up-front lag in these machine learning applications, of course—if you don’t use them, they can’t learn what you want. But I could never stick with Wavii long enough to get it tuned up better than my Twitter feed, which is where I get most of my news.

On the other side of this field are news services that take the old-school approach of … Next Page »

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