Google famously ordered the importance of Web pages based on the quantity and quality of other links pointing to each page. In this era of the Web's evolution fueled largely by social media, are there better ways to rank this consumer-generated content?

Mindvalley presents an alternative. Last week, the startup launched 30 new sites powered by SocialRank, its technology that culls lists of the most important stories from blogs in a range of categories. To provide a basic framework, while Digg ranks stories based on the explicit votes of its members, SocialRank culls top stories based on the comments on posts, backlinks from related bloggers, and other factors that are more implicit measures.
How does SocialRank compare with PageRank? Can algorithms pick stories as well as humans? Is this system ripe for gaming? Find out in these excerpts from an interview with MindValley co-founder Vishen Lakhian; if you're interested, Lakhian answers even more questions on my blog.

Can you shed more light on how the SocialRank algorithm works for ranking stories? How much of it is inspired by Google's PageRank?

SocialRank measures the quality of blog posts. It helps readers identify what posts are most worth their time. You might be interested in a particular field -- say photography, marketing or blog monetization. For each of these fields there exist some 1,000 blogs and over 100 posts per day being churned out on the net. This is a lot of choice.

But it also causes a problem. You have limited time. How do you filter this massive amount of new content and identify only the very best content in each of these categories? SocialRank does this for you. It's a powerful filter which shows you the hottest posts in different niche categories.

Now there have been many other attempts to identify how hot a blog posts it. There are sites like Digg, where users vote on stories. They are also sites that give you widgets that you can install on your blog and they use this data to serve up your most read blog posts.

Here's how SocialRank is different: We get data on blog posts without the bloggers having to install anything. There is no need for a blogger to add a widget or for people to vote on the post. Once your blog is added to SocialRank, we can automatically study and analyze your post and make a conclusion on how important it is.

This gives us the power to launch hundreds of niche Web sites tailored to different blogging categories. For example, TomorrowsBrands.com lists the most important posts in the field of advertising. MarketingLens.com lists the most important posts in the field of online marketing, copywriting, SEO, and email marketing. We have hundreds more launching, covering such diverse topics as Motherhood, Photography, Manga, Atheism, Burma, and U.S. Politics.

What makes a great story for these algorithms -- say, the MarketingLens algorithm as an example?

People have tried creating filters for niche content before. With open-source software like Pligg, hundreds of people tried to launch their own mini-Diggs catering to niche topics like real estate or marketing. But the efforts usually failed. The reason is that voting rarely works in niche topics.

Even on a site like Digg, very few visitors bother to cast a vote. I read somewhere that it's close to 3%. Now this works fine for Digg because their user base is so large. Their 3% is still a big crowd. But for smaller niche sites, the 3% is just not big enough to generate accurate rankings. It's a catch-22 situation. They need a big audience to make their voting count. But until their voting starts to count, they can't attract a big audience. So the vast majority of these sites struggle. By eliminating voting, but instead using SocialRank, we can launch these niche sites, with good data, even BEFORE an audience arrives.

How well can you ensure that the algorithm picks stories as well as a human would?

I think they would correlate quite well. When we designed SocialRank, we tried to make it emulate human nature as much as possible. How do humans react to good stories? Well, they tend to resyndicate or blog about these stories. They also tend to comment on the stories. Or save them in social bookmarking sites. Or tell their friends about them. We look at this sort of behavior.

For example, you'll notice that SocialRank studies the comments on blogs and make a note of what stories are getting the biggest amounts of comments over a given time frame. This is just one of the ways we identify a hot story.

Is there any way bloggers can game the system? Do you anticipate any problems there?

They will always be spammers trying to game the system. Yes, some bloggers will try. But here's where we have a slight advantage: On a site like a Digg, you can hire professional "voters" to vote up your stories. But on SocialRank, one of the big things we look at are comments on your own blog post. So to game SocialRank, you would need to hire people to comment on your own post. In other words, you'd have to hijack your own blog to game the system.

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