Life in the network: the coming age of computational social science
David Lazer, Alex (Sandy) Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, and Marshall Van Alstyne
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We live life in the network. When we wake up in the morning, we check our e-mail, make a quick phone call, walk outside (our movements captured by a high definition video camera), get on the bus (swiping our RFID mass transit cards) or drive (using a transponder to zip through the tolls). We arrive at the airport, making sure to purchase a sandwich with a credit card before boarding the plane, and check our BlackBerries shortly before takeoff. Or we visit the doctor or the car mechanic, generating digital records of what our medical or automotive problems are. We post blog entries confiding to the world our thoughts and feelings, or maintain personal social network profiles revealing our friendships and our tastes. Each of these transactions leaves digital breadcrumbs which, when pulled together, offer increasingly comprehensive pictures of both individuals and groups, with the potential of transforming our understanding of our lives, organizations, and societies in a fashion that was barely conceivable just a few years ago.
The capacity to collect and analyze massive amounts of data has unambiguously transformed such fields as biology and physics. The emergence of such a data-driven “computational social science” has been much slower, largely spearheaded by a few intrepid computer scientists, physicists, and social scientists. If one were to look at the leading disciplinary journals in economics, sociology, and political science, there would be minimal evidence of an emerging computational social science engaged in quantitative modeling of these new kinds of digital traces. However, computational social science is occurring, and on a large scale, in places like Google, Yahoo, and the National Security Agency. Computational social science could easily become the almost exclusive domain of private companies and government agencies. Alternatively, there might emerge a “Dead Sea Scrolls” model, with a privileged set of academic researchers sitting on private data from which they produce papers that cannot be critiqued or replicated. Neither scenario will serve the long-term public interest in the accumulation, verification, and dissemination of knowledge.
What potential value might a computational social science, based in an open academic environment, offer society, through an enhanced understanding of individuals and collectives? What are the obstacles that stand in the way of a computational social science?
From individuals to societies
To date the vast majority of existing research on human interactions has relied on one-shot self-reported data on relationships. New technologies, such as video surveillance, e-mail, and ‘smart’ name badges offer a remarkable, second-by-second picture of interactions over extended periods of time, providing information about both the structure and content of relationships. Consider examples of data collection in this area and of the questions they might address:
Video recording and analysis of the first two years of a child’s life (1)
Precisely what kind of interactions with others underlies the development of language? What might be early indicators of autism?
Examination of group interactions through e-mail data
What are the temporal dynamics of human communications—that is, do work groups reach a stasis with little change, or do they dramatically change over time (2, 3)? What interaction patterns predict highly productive groups and individuals? Can the diversity of news and content we receive predict our power or performance (4)?
Examination of face-to-face group interactions over time using sociometers
Small electronics packages (‘sociometers’) worn like a standard ID badge can capture physical proximity, location, movement, and other facets of individual behavior and collective interactions. What are patterns of proximity and communication within an organization, and what flow patterns are associated with high performance at the individual and group levels (5)?
Macro communication patterns
Phone companies have records of call patterns among their customers extending over multiple years, and e-Commerce portals such as Google and Yahoo collect instant messaging data on global communication. Do these data paint a comprehensive picture of societal-level communication patterns? What does the “macro” social network of society look like (6), and how does it evolve over time? In what ways do these interactions affect economic productivity or public health?
With GPS and related technologies, it is increasingly easy to track the movements of people (7, 8). Mobile phones, in particular, allow the large scale tracing of people’s movements and physical proximities over time (9), where it may be possible to infer even cognitive relationships, such as friendship, from observed behavior (10). How might a pathogen, such as influenza, driven by physical proximity, spread through a population (11)?