Digg

January was a good month for Digg with an increase in page views by 35% (our highest level since October of 2010) and Facebook referral traffic up by 67 percent. It also marked the first month for Digg Social Reader, our Facebook Timeline App.

But increased engagement has only been half of the benefit. The greatest learning has been a better understanding of the type of news stories that people add to their Timeline and why.

At Digg, we spend a lot of time trying to understand the strength of news signals. Diggs, Likes, Tweets and LinkedIn Shares are all ways of understanding a news story’s relative importance and interest to a large audience. But most recently, what has been the most compelling are the stories that people are adding to their Timeline, both as Reads as well as Diggs, Comments and Submissions.

Specifically, what are the types of stories that people are most likely to add to their Timeline and the Facebook Ticker and what can we learn from that choice?

For those of us in the news business, the ability to accurately measure popular sentiment is gold to understanding what people want to read.

Stories that readers add to their Facebook Timeline closely resemble what you might talk about at a party or when grabbing a drink with your co-workers. Headlines are usually pretty safe topics— not politics, religion or anything that might cause debate. In order, they’re most likely about technology, offbeat news and world events. Specifically, the headlines that were added to readers’ Facebook Timeline the most in January were breaking news on SOPA legislation, the best iPhone& iPad apps and a girl that ate nothing other than chicken nuggets for the last 15 years.

As compared to stories read on Digg ( without Digg Social Reader turned on)— there are two main differences. Entertainment stories were 14 percent of all stories read but less than 4 percent of those added to the Timeline. Likewise, political stories comprise less than 2 percent of those added to a user’s Timeline but close to 10 percent of what people read. The differences are significant enough to begin to predict a new type of reading behavior.

Finally, what’s interesting about the data is how high quality these stories are. And by that, I don’t mean stories that will make you smarter or even get you dates. I mean stories that are the most likely to represent popular sentiment, unaffected by optimization or online tricks that publishers use to drive traffic to their site.

A scalable, human curation model of news is taking effect. And we’re just getting started.