Does Social Graphing Offer Publishers A Better Way to Distribute Content?

Publishers and their business models have always been built around distribution channels. In days of yore, magazines and newspapers were sent out, and audiences, revenue streams and content formats hinged largely on how and who received them.

With the rise of online publishing, followed by a multitude of mobile devices, web apps, social and messaging platforms, and now the rapid emergence of voice assistants, there are nearly endless ways audiences can consume the content publishers produce.

James Slezak, managing partner and founder of New Economy Labl

What that means for publishers is they are compelled to make their media available and easy to find to everyone, everywhere. Meanwhile each output calls for a different format and tone of voice, and has different user expectations and interests. Recreating and repackaging content for every possible output – from mobile to desktop, Snapchat to Twitter, digital to print – has become untenable. Turns out distributing content can be a drain.

It was inevitable that as digital platforms diversify and proliferate, approaching audiences by channel is becoming increasingly untenable. “There are just too many channels,” says James Slezak, managing partner and founder of New Economy Lab, and former VP and chief of operations for NYT Global.

Publishers need to find a better way of delivering their content to the readers that are going to be most receptive. Slezak suggests that “social graphing” is a tool publishers will be able to use to make their content distribution more intelligent, efficient and effective. The ability to analyze the social graph, directly observing communities that people self-organize into and the clusters of interests and beliefs within them, are powerful weapons for getting the right media and messages to the right people – a core need for publishers and their advertisers. Slezak explored social graphing and it’s potential impact on the media business at Publishing Executive’s FUSE Media summit last fall.

Watch Slezak’s full presentation at the bottom of this article or click here.

Publishers have seen the proliferation of devices, operating systems, and platforms in the past 10 years.

Can We Predict What Someone Will Read Online?

Understanding exactly how social graphing works — and how publishers might one day apply it to make their lives easier and their audience targeting more accurate — is a bit complicated. In the simplest of terms, a social graph is nothing more than an illustration of relationships between people on the internet. You might even think of it as the internet version of a family tree. Social graphing (part the larger practice of social network analysis) uses data science to analyze and map social networks in order to better understand the relationship between users, and further, to illustrate the manifestation of communities.

Due to the many promising applications of social graphing, Slezak says data scientists have been working to crack it for years. “And I think it's starting to show some interesting signs of opportunity for people.”

So how can this social graphing help publishers predict what each reader wants to read? According to most social psychologists, says Slezak, "Whether or not you're going to read a certain article is a function of your tribe."

In other words, whether or not you're interested in, say, an article about climate change has a lot less to do with whether you've spent time considering how important climate change is to you. Instead, Slezak suggests, it's much more a function of your social network. "You know, who are my friends? Who's my community?"

The theory holds that by using social graphing to drive content distribution, publishers will be able to find the individuals and communities that will be most receptive to that content.

Now consider the method Slezak refers to as "the traditional four ways of doing audience segmentation." These are the ways in which publishers have historically thought about how to target and find potential readers, and Slezak claims they aren't especially helpful when it comes to identifying a reader's community or tribe.

Slezak says the traditional techniques publishers have use for audience segmentation and targeting, typically based on geographic, demographic, psychographic or behavioral data, fall short. "All of these things have had major limitations right from the beginning.”

Geography and demographics, Slezak points out, are often largely irrelevant for the purposes of distributing content. Using the aforementioned climate change article as an example, "It's probably just as relevant to a 17 year-old student in Wyoming as it is to a 60 year-old person in Auckland, New Zealand."

And what of the psychographics and behavioral targeting? “The big problem with that,” says Slezak, “is that of course, it's largely it's a lot of guess work. It's helpful work, and it's often the best that we can do. But it's largely guess work."

Social graphing uses data science to analyze and map social networks in order to better understand the relationship between users, and further, to illustrate the manifestation of communities.

Social Graphing Sounds Great — How Can We Use It?

There are multiple reasons to proclaim social graphing a potential game-changer in audience targeting and digital content distribution: It's a process that can identify niche audiences that have the potential to grow. It's an accurate predictor of the sort of content a publisher should create. Social graphing also allows audience segmentation to be based on real-world affiliations. And it can go a long way toward reducing the cost of content distribution.

And yet unfortunately, at the moment there's not a great deal publishers can actually do with social graphing. But Slezak says there is a set of real goals for those developing social graphing, which are increasingly becoming achievable. He outlined them as follows:

Goals of Social Graphing in Publishing Industry

• Segment audience based on real affiliations
• Target effectively in a peer distribution environment
• Identify niche audiences that can grow
• Predict what content to create, including native
• Reduce cost of delivering content
• Measure impact of content on beliefs and affiliations

The end game of social graphing, of course, is to empower publishers with practical tools that will empower them to get content into the hands of those people that are highly interested in that content. To that end, Slezak says that a lot of fruitful research is being conducted by the likes of MIT Media Lab and the Berkman Klein Center, as well as Facebook and Yahoo. On the product side, companies like Swayable (which Slezak’s New Economy Lab is working with) and Affinio are working to weaponize social graph science for media companies. Meanwhile, books like Social Physics: How Good Ideas – The Lessons from A New Science and Malcom Gladwell’s Tipping Point are popularizing social network analysis.

“I think we're getting near a peak where this technology becomes useful,” says Slezak. “Ultimately, this really is the reality of how people are connected to each other. So there has to be some way to turn this into a mechanism for getting content to people."