If you count all the different media-related apps for the iPhone and iPad, there are almost an infinite number of ways to consume the news — some are specific to a particular newspaper or magazine, while others try to aggregate different sources of news for you. What the media industry really needs is some way to filter all of that information in useful ways, and recommend things you might not have read yet. But so far, despite some new entrants on the scene such as News360, which launched an iPad news-reading app at DEMO today, social networks like Twitter and Facebook have proven to be the best way of getting recommended content.
News360, which is based in Russia, has had an iPhone app for some time, but launched a much more ambitious iPad version of its service at the DEMO conference in California. The company says that its news app uses “sophisticated AI technology” to pull in articles from a thousand worldwide news sources, at which point these pieces of content are “algorithmically evaluated for news freshness and source credibility.” The primary user interface for the app can be switched based on which way you hold the iPad: in portrait mode it is a list of headlines, and in landscape mode it is a series of photos representing the different news stories, which sort of float past in a stream.
The app provides an excerpt of the story, with a full version that can be read in a built-in browser, and it also groups related stories from other publications that are about the same topic (although it suffers to some extent from the “Google News problem,” in which multiple versions of the same newswire piece show up under the names of dozens of different media outlets). And in an interesting twist, News360 adds links from common keywords or names to information tabs that pull in content from Wikipedia and Freebase. Users can also link to a TripIt account and have the service pull in news from locations where they will be travelling.
The problem with News360 is essentially the same problem I have with other so-called smart aggregation services and apps, including a soon-to-launch project from The Washington Post called Trove (others launching similar attempts include AOL and Yahoo). Although aggregation services are nice, they are of limited use unless they can provide filtering and recommendation — in other words, cut through the noise and learn what I like. And that is very difficult to do. Even the New York Times, which has information about me based on my participation in its Times People social network, does only a so-so job of this, in the sense that what it recommends often seems fairly random.
Arguably the biggest source of recommendation-type data is Facebook, which can see when your friends like something, when they share it, etc. Huffington Post has driven a lot of traffic to the site by making smart use of Facebook integration to recommend stories that people you follow have liked or read. The reality is that social networks like Twitter and Facebook are inherently farther ahead when it comes to recommendations because of all the social signals that are embedded in my social graph, the relationships with the people I follow and my friends and social network.
There are some services and apps that make use of the links that get passed around via Twitter and Facebook — there’s Tweeted Times and Paper.li for showing you links from Twitter, and PostPost does something similar for Facebook links, producing a kind of personalized newspaper. News.me, the social news platform that Betaworks and the New York Times are launching soon, takes a different twist. And Flipboard pulls in links from your Twitter stream, your Facebook graph and RSS feeds and shows it to you.
But no one is really doing much when it comes to recommendations. I’ve tried playing with Trove, and it is not much better than a random sampling of news that is being shared on the web — and News360 seems equally haphazard. It’s possible that they could get better over time, of course, although there doesn’t seem to be any way to tell either service that it is wrong when it suggests a particular story to you. And Flipboard in particular seems well placed to do something smart with the content it is pulling in, since it acquired a semantic engine called Ellerdale, but so far hasn’t done much with it.
Others are trying to solve the recommendation conundrum, including a startup that is still in stealth mode called Woven, which former Flightcaster staffer Bradford Cross is developing as a kind of social twist on the idea of a “smart agent” that adapts as it learns about you. And Google has tried to add social signals to Google News, but without much success. So far, if you want recommendations about what to read, your Twitter stream and Facebook graph are probably the best solution — and anyone who wants to do better is going to have to leverage both of them to do it.
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