Deriving real value from the social graph

As the physical presences formerly known as “human beings” undergo transmutation into electronic avatars on the realtime social network, the ability to automatically track and analyze their “movements” and “relationships” becomes an increasingly attractive value-mining opportunity. This opportunity exists today, but what’s required to exploit it is the connection of the rich data collected on avatars’ activities with data collected on various salient economic variables. Such a connection of datasets would allow much more precise estimates of the economic value of, for example, “friends” and “followers” in the context of both consumer markets and labor markets and, in turn, the ability to better measure, incentivize, and in general manipulate interactions on the so-called social graph.

A new report from an industrial-academic team of MIT and IBM researchers begins to give a sense of the historic scope of this social-engineering opportunity, at least in the labor context. As the researchers note, in their introductory remarks, there exists “a large body of literature on social networks and organizations that describes the benefit of social networks on work performance in various settings,” but “little research leverages the ample data that are created by people’s interactions, such as e-mail, call logs, text messaging, document repositories, web 2.0 tools, and so on.” The “gap,” they note, is “problematic.” Fortunately, “recent empirical work has started to capture real-time communication between people in various settings.” For example, “recent advances in information technology give researchers the opportunity to solicit real-time email communication data. Since email archives record detailed communication logs, such as who has emailed whom, the exact time of the interaction, and the content of the exchange, using email archives to construct social networks allows researchers to eliminate any errors and bias that are introduced through self-reports.”

But the automated mining of email communications is only the tip of a much larger iceberg of opportunities for analyzing personal data sources on the realtime network. One “privacy-preserving” system for “organizational social network analysis” has been “deployed in more than 70 countries to quantitatively infer the social networks of 400,000 employees within a large [consulting] organization. It uses social sensors to gather, crawl and mine various types of data sources, including content and properties of individual email and instant message communications, calendars, organizational hierarchical structure, project and role assignment.”

The research team analyzed the data collected by this system in order to “examine the effect of network characteristics on revenues for both employee and project networks” as well as on “individual employee productivity.” The researchers discovered that “having strong connections with contacts in the network does not necessarily have any significant impact on performance,” but that having contacts with an “additional manager is associated [with] $588 additional monthly revenues,” pointing to the importance to value creation of “having strong connection to people in the position of power.” The fact that, in business, it’s “who you know” may not seem startling, but the ability to precisely quantify, in dollar terms, the value of instances of “friending” gives a powerful hint of the future benefits that will accrue to those institutions able to pursue the automated collection and analysis of data, with or without “privacy-preserving” controls, on the behavior of “people” as they navigate the vast and radically transparent avatarian realm.

As Stephen Baker notes, in reporting on the MIT-IBM study, “Users of social media rack up LinkedIn contacts, Facebook friends, and Twitter followers by the hundreds, if not thousands. But figuring out how big a difference all those contacts make in a person’s life, financial or otherwise, is a far murkier matter.” Thanks to powerful data-mining software deployed by corporations and other interested parties, the murk may at last be lifting. Baker concludes: “more companies are sure to study the company we keep – and even attempt to calculate how much each friendship is worth.” The creation of the social graph, it’s clear, was only the first step in a long process of economic optimization.

This post is an installment in Rough Type’s ongoing series “The Realtime Chronicles,” which began here.