“Mirror mirror on a chip, tell me who is the most hip…?”
Using social badges that measure face to face interaction on the microscopic level allow us to predict patterns of collaboration and gain insights into how we work together on levels not possible before. At the MIT Center for Collective Intelligence we have recently experimented with what we call microscopic dynamic social network analysis.
GPS satellite based navigation systems tell us where we are and how to get where we want to go. In our research we used a similar people-based social navigation system develop at the MIT Medial Lab by Sandy Pentland’s team to better understand our position in social networks. Using social badges – body-worn sensors – we measured physical interaction of co-located people to better understand who they are, and therefore allow them to better navigate in their own social network.
In a research project with 22 study subjects, who wore the badges during work for one month, we were able to predict social characteristics such as extroversion, neuroticism, openness, and agreeability based on microscopic social network analysis. We obtained control measures of these values with a standard psychological test NEO-FFI). High contribution index was positively correlated with extroversion, and negatively correlated with neuroticism. This means that the more people looked their communication partners into the face, the more of an extrovert they were. The less they looked them into the eyes, the higher was their score on the neuroticism test. Fluctuation in betweenness centrality was positively correlated with openness, and negatively correlated with agreeability. In less scientific language: the more they changed between being in the center of the conversation, and by withdrawing into their offices, the more open to new things they were. The steadier their communication pattern, either as a socialite or a recluse, the higher their agreability score. We were also able to obtain correlation between social network position and job satisfaction, and extroversion.
Of course this technology has to be used very carefully, to avoid the risk of intruding into the privacy of the individual. In our project we have alleviated this risk by only sharing individual results with each affected individual, and giving a condensed view without individual identification to management. So far study participants have reacted very positively to the insights they gained about their own communication behavior.
Microscopic social network analysis can be used to complement proven psychological tests such as the FFI. It could be used, e.g. as a further input to identify people suitable for certain professions, for example identifying the most agreeable candidates among potential recruits as police officers. By simply wearing social badges, a user will finally be able to answer question like “Do I have more of an introvert or an extrovert communication style? What personality types do I have to bring into a meeting to make it more productive? How can I change my personal communication behavior to be more efficient? What leadership styles are most effective for a certain situation?” We hope that future research will help organizations become more innovative and productive by exploring their hidden social structures in a virtual mirror – helping members of an organization to better understand their hidden social characteristics to improve the overall organization.