Wavii Builds a Facebook-Style Feed for All the World’s News
After talking with the guys behind Wavii, a Seattle startup that has built a Facebook-style feed for all the world’s events, I had two somewhat conflicting impressions: Wavii is potentially good medicine for information overload, and at the same time another sign that this modern affliction has us tightly in its grasp.
Wavii delivers a constant stream of updates on whatever you’re interested in, though celebrity gossip, entertainment, technology news, and politics dominated the demonstration I saw. The company’s app, launching today in its second iteration for the iPhone, is “kind of a navigational layer on top of content,” says founder and CEO Adrian Aoun.
But it’s about much more than just navigation. In some ways, Wavii is the Veg-O-Matic of Internet content consumption. It slices and dices news into easily consumable bits. It’s like Facebook. It’s like Google, like Twitter. But better, Aoun says.
There’s no shortage of ambition here. And Wavii is in a space with no shortage of competition.
The 25-person company draws inspiration, technology and talent from the likes of Microsoft, Amazon Web Services, Last.fm, and Noam Chomsky (Aoun’s father, Joseph Aoun, is a linguistics professor and now Northeastern University president who studied under the prolific scholar at MIT). Angel investors include Lotus founder Mitch Kapor, PayPal co-founder Max Levchin, Shawn Fanning of Napster, Keith Rabois, Ron Conway, Dave Morin, Crunch Fund, and others, Aoun says. The company has raised “upwards of $10 million,” according to a PR pitch. Aoun wasn’t going there. “Not to be entirely obnoxious, but just to be blunt, we try not to get into the details of our financing.”
Wavii would make money by besting the big players in online advertising, and, as a secondary business, licensing the structured data it is amassing. There are also hints that the powerful learning machine at the company’s heart could have broader applications.
In advertising, Aoun says the company can combine the context that makes Google search ads valuable with the demographic information compiled by Facebook, which is effectively a proxy for what people are interested in.
“We somewhat have the best of both worlds, right? We’ve got context and we’ve got a strong interest graph, or interest profile about our users, and so, we think that advertising will be a very good fit for us,” Aoun says.
But he insists that after the better part of four years in business, “we’re not pursuing revenue right now.”
“Probably in the next couple of years is when we’ll start to actually try and make money. But not quite yet. We want to focus on just kind of perfecting the product and building up our user base first,” Aoun says.
That timeline sounds more like a cleantech company model than a hot app developer. But as Wavii head of product and operations Dan Lewis points out, the company is attempting a heavy technical lift in the realms of big data, information extraction, and machine learning, among others. “A lot of research we’ve done with the technology was not like a six-month project, it was a multi-year project,” he says.
To create that Facebook feed for the world’s events, Wavii had to find a way to replicate the work that a billion Facebook users do today almost by rote. Those feeds of your friends’ comings and goings, affinities, moods, pictures and so on are all based on manual entry of structured data, augmented with context such as maps, related pictures, and other information from Facebook’s vast and expanding database. This is perhaps easily forgotten as updating Facebook—entering that data—has become a part of the daily routine.
Wavii’s system—which lives on Amazon Web Services; Wavii claims not to own a single server or desktop computer—crawls the “real-time Web,” meaning “news articles, blog posts, Tweets, videos, anything we can get our hands on the second it’s coming online,” Aoun explains. It harvests from that mass of content the information needed to create news feeds. These amount to quick summaries of events with the salient details illuminated for easiest consumption. It frees users, Aoun argues, from a world where we’re “still stuck… reading news articles to consume everything”.
Of course, there’s still a place for deep reading, he says. From within the Wavii news summary, you can dive in to the source content from which it is synthesized. “My goal is actually to drive traffic to” the original content creators, Aoun says, noting that the system pulls up relevant stories from the past to add yet more context to the current news, driving traffic to a publisher’s archives.
But you don’t have to dive in. Just like you can get up to speed on the Facebook lives of hundreds of people in a few minutes each morning, Wavii offers the prospect of a very fast skim of the news you’re interested in, skipping across the surface, sucking up the salient details, and heading off on your way.
“We want to get away from forcing users to read every single Tech-Crunch article and instead just let them focus on the ones they want,” Aoun says. “So our goal is to create higher quality engagement with fewer pieces of content.”
Aoun readily admits that this sort of synthesis is already available for some things. Google “MSFT stock” or “Seattle weather” and the top result is a real-time quote or a four-day forecast, with links to follow for more details.
“The problem is that they only do it for a few things, right?” Aoun says. Wavii covers thousands of topics.
What about Twitter? “I go to Twitter to get the high level. Except that the problem with Twitter is that it’s fundamentally broken when I get this high level, because I still have to kind of click into each article,” he says. And, “you can’t follow your actual interests.”
Lots of companies are trying to aggregate and personalize news, and have been for some time. The ones that appear most like Wavii are Trapit and Prismatic. There’s also the crop of social newsreaders Flipboard, Zite, and Pulse.
After launching in April, Wavii found significantly more people were using the mobile version than the desktop/Web. The company spent the last six months improving the mobile app, with changes to the look and feel, facial recognition to cleanly present the relevant parts of photos, and more news events and topics to follow. (Many of the features promised for the new iPhone app did not appear to be up and running during a recent visit to the Web version.)
Register with Facebook or Twitter and Wavii scours your profile for details to help identify news and events you’re interested in. (You can also register with e-mail—a new feature along with Twitter registration—creating more of a blank slate Wavii profile.) Naturally, you can search; comment on and share what you’re following both on Wavii and other social networks; see what other Wavii users are following and saying; and quickly follow new people, topics, and broad categories, which Wavii will suggest based on your recent activity.
“We’re smart enough to help introduce you so you don’t have this myopia problem where it’s like the things I know and that’s all I get,” Aoun says.
Wavii also lets you follow types of stories, such as interviews, startup acquisitions, movie trailer releases, product launches, or celebrity engagements.
The company’s system searches for patterns in language and information relevant to specific types of news events. In the case of an engagement, for example, it has been programmed—through a subset of machine learning called active learning—to filter out instances when one party is, say, “engaged” in a heated debate with another, and only surface wedding engagements. “It will learn these subtle differences of language,” Aoun says.
Having learned the language, Wavii’s system begins to perform the tasks of a human news editor, selecting relevant details and making judgments about credibility. (As such, it stands in contrast to the efforts of Circa, for example, which uses teams of human editors to put easily digestible news chunks on mobile devices.)
Unlike other information extraction systems, which may recognize the verb in “A married B,” Aoun says Wavii’s system knows what married means and will look for dozens of relevant details to fill out a template for news of a wedding: Who was wed? Who was in the wedding party? How big was the ring? Who designed the dress?
It’s (relatively) easy to aggregate details about an event that’s been covered thousands of places on the Web, but to be valuable as a news source, Wavii has to gather those details early in the news cycle, when information is scarce, and with a high level of accuracy. To accomplish this, Wavii models the reliability of particular news sources for particular topics to help establish a confidence score. CNN would rate higher on politics than Dan Lewis’ blog, for example. It also filters out wire services stories that appear in hundreds of different places with only a few words changed to avoid skewing the confidence score through repetition.
“We use these things, what we think of as like multiple evidence points to lead us to conclusions, and when things pass certain thresholds, we say, now our system is going to believe it,” Aoun says.
If an event fails to meet that threshold, or sources appear to be speculating, Wavii adds a purple “rumor” tag, until the feed is updated with more data.
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