Since the beginning of the new year, there has been much talk of Facebook’s Graph Search and the company’s attempt to quantify the information goldmine that makes up a social network. Analyzing how individuals negotiate their online networks is a critical challenge for many tech companies if they want to make their services profitable. However social scientists are using a new method of communication study, known as Social Language Network Analysis (SLNA), that may completely re-imagine how we understand and approach group productivity itself.
In the paper “Creation Nets: Harnessing the Potential of Open Innovation,” John Hagel III and John Seely Brown argue that firms must not only exploit external ideas to advance their own technology — a concept originally suggested by Henry Chesbrough — but that organizations must also dissolve the borders between their own enterprise and the rest of the world. According to Hagel and Brown, as change accelerates, success is determined not by the stocks of knowledge that an organization has, but rather by its ability to connect with others in order to create new knowledge. In order to most effectively do this, institutions must develop “creation nets,” a form of open innovation that utilizes a set of institutional mechanisms to leverage the distributed and collaborative abilities of external entities. Rather than only sharing intellectual property with a competitor, or creating a few relationships with outside experts, organizations must create or become part of an exogenous innovation ecosystem.
One of the main challenges in harnessing creation nets is finding ways to manage the far-reaching nodes of the vast development and supply chain. Some propose establishing effective performance feedback loops, where participants can see what effect they are having within their creation nets. This type of work seems relatively easy to do when it comes to performance in a concrete sense, such as with an open-source program: How many bugs are being removed? How close is the code from being completed? However, when one is dealing with less tangible issues, such as innovation and creativity, how can these feedback loops be generated?
One possible answer to this question is Social Language Network Analysis, a social science methodology for studying groups and their processes. Introduced in the journal Computer Supported Cooperative Work, SLNA combines social network analysis with a form of social language processing that identifies “psychological, social, and emotional undercurrents in interpersonal communication with the structural insights of network analysis.”
Related areas of study, such as sociolinguistics, already examine the nodes and ties of social networks and how these relate to an individual’s use of language. Such research examines how an individual’s speech changes based on the social status of those with whom they are speaking. SLNA does the inverse of this, where the study of language variation is used to explicate the individual’s social standing within a network.
For instance, a pilot study of SLNA used conversations from the internal chat client of a scientific research and development (R&D) organization to evaluate the group-level attitude toward each of its members — a sort of unspoken hierarchy based on expertise and respect, rather than an individual’s place on the organizational totem pole. The researchers used such linguistic measures as how an individual uses the first person singular pronoun (e.g. “I”, “I’ve”, “me”, “mine”), which has been shown to increase as a speaker interacts with someone of higher status. The researchers found that the technical skill-based roles, such as those of programmers and analysts, elicited higher levels of respect than those of more managerial roles. Such findings correspond to previous ethnographic studies of cultures in R&D organizations that show that respect for technical expertise often supersedes that of middle management.
At the moment, our electronic interactions with colleagues and superiors are based on the literal information that we relay. The nascent methodology of SLNA offers the possibility of understanding social networks in terms of the social, psychological, and emotional information that often lies just below the surface of interactions. Imagine how powerful a tool could be that translates what we say into what we actually mean. Perhaps we could begin to more deeply understand how bonds are formed between individuals, or how teams are strengthened through social support. Through SLNA, social scientists may begin to understand the nuanced dynamics of creation nets, demystifying the alchemy of one of our most important skills — cooperation.
 Scholand, A.J., Tausczik, Y.R., & Pennebaker, J.W. “Social language network analysis.” CSCW 2010. February 6–10, 2010. p. 23-26