Like almost everyone I know, I’m a total content junkie. I read everything I can get my hands on, mostly tech and startup stuff, but with frequent side trips into economics, behavioral psych, education, endurance sports, music, organizational behavior, and… well… pretty much anything that catches my eye.
When I first discovered RSS I was ecstatic. Instead of scouring the web to find great content I could now create my own private newsfeed and stay on top of all the authors and topics I cared about. I treated my reader like a second inbox (see where this is going?…) and loved how efficiently I could churn through content. But the more I read, the more great new authors I found and added to my reader. Pretty soon I was struggling to keep up with the flow, declaring RSS bankruptcy several times a week just to stay current.
Then Twitter showed up, and everything was OK again… for a while.
Since many of my favorite Twitter people are also my top blog reads, all I had to do was switch my read behavior from RSS to Twitter to get back to some kind of flow. But now the best content was now buried under shortened links. And saving and sharing my favorite stuff was much harder too. I didn’t want to broadcast all my likes via Twitter so I found myself sharing a ton of stuff by email, effectively burying it for later recall. I also kept adding follows, building up an even more overwhelming flow than I had in RSS. And every day it seemed to get a little worse….
So when a team of insanely smart guys calling themselves Perpetual P showed up for TechStars Seattle last summer talking about personalized content discovery, my ears perked up immediately.
The team – Kareem, Nav and Nicolae – all had deep CS backgrounds and loved mathematical solutions to hard problems. But they also grokked the critical role that human relationships play in prioritization and decision making. Their idea was to build a software system that enabled and amplified human content curation, extracting patterns that could be used to power automated content discovery, but leaving room for people to steer and shape the conversation, and to give feedback to each other on the content they shared. Over time, they reasoned, this system would build an “interest graph” analogous to Chris Dixon’s “taste graph” idea.
Almost a year has passed since I first met the guys behind what’s now called The Shared Web. Curation has become a hot topic for media analysts and investors. Explosive adoption of the iPad as a media consumption device has steered attention toward beautiful mobile read experiences that tap the social stream to produce personalized feeds – think Flipboard, Zite and News.me.
As cool as these products are, none of them does what I really need them to:
- Show me what I need to read.
There’s waaay too much content out there – even within my social graph – and most of it doesn’t interest me. Don’t just play back to me everything my friends have shared. Roll up shares across my network and learn my preferences to show me the things I really need + want to know right now. - Help me discover killer stuff I wouldn’t find on my own.
I’m a huge believer in Steven Johnson’s framework for creative breakthroughs being triggered by collisions between ideas from disparate domains. A really smart content discovery platform wouldn’t just amplify my social echo chamber, but would use my known preferences and interests as a jumping-off point to expose relevant new topics and ideas. - Give me embedded tools to share, save and interact with others around the content I find.
I read to feed my brain, discover new patterns and spark conversations with friends and co-workers. Not every content item merits deep engagement, but when I find one that does I need effortless ways to kick off a conversation, share it with the relevant audience and flag it for future reference, all embedded in my read flow so I can stay in the groove.
The Shared Web is a social news product – think of a social Reddit, or a relevant and spam-proof Digg (thanks to real identities and social filtering). The premise is simple: you choose the topics that interest you and The Shared Web shows you the news items you need to read on those topics. Content recommendations are based on learning algorithms that take into account not just what’s being shared within your social social graph, but also thematically related content that’s being shared across the entire community.
The Shared Web promises three things:
- You’ll never miss the big stories of the day on the topics you care most about
- You’ll also discover content that’s dead on with your interests, but that you probably wouldn’t have come across within your existing social graph
- Whenever a content item hits a nerve, you can quickly and easily (a) thank the friend who shared it, (b) recommend it to friends, and/or (c) save it to read again later.
If you’re passionate about about innovation in digital media (and worry about confirmation bias or narrowcasting as a risk of social curation systems), I think you’ll like what The Shared Web guys have put together. I’ve included an (expiring) link below to give interested folks a sneak peek at the private beta.
I liked The Shared Web well enough to sign Founders Co-op on as an investor in their seed round. I’d love love to hear your thoughts once you try it out. But act fast, the link above expires in a few days!