Computational Social Science

We use rigorous observational methods, often involving datasets we ourselves collect (but also sometimes involving partnerships with industry), to understand the structure and function of social networks. It is our belief that careful observation of the natural world is a proper predicate for subsequent experiments of the kind we also conduct.
Beginning in 2007, we published an extensive set of papers using the Framingham Heart Study social network (a version of which we have placed in the public domain) as well as several other large-scale observational data sets, exploring social contagion for phenomena as diverse as obesity, smoking, drug use, sexual practices, and happiness. At present, our observational work is mainly focused on the developing world. Many developing world contexts not only have a greater burden of health problems, but also tend to have more traditional social systems with less modern technology. Such settings are ideal for examining how social networks affect health outcomes. Sample sizes for these projects range from 2,000 to 40,000 people, from 4 to 160 villages. We've examined latrine use in India, AIDS treatment in Uganda, and intimate partner violence (IPV) and neonatal care in Honduras.
We have also studied much larger systems online, involving hundreds of thousands or even millions of people. We live in a world of pervasive data, and this has led to the advent of a new kind of computational social science. If one had asked social scientists even 20 years ago what powers they dreamed of acquiring, they might have cited the capacity to inconspicuously track the behaviors, purchases, movements, interactions, and thoughts of whole cities of people, in real time. Of course, this is exactly what is possible now that so many of us leave digital breadcrumbs as we move through our lives. Such "massive/passive" "big data" is revolutionizing our ability to understand behavior.
As part of this research thrust, we take advantage of new tools in computational social science, and our access to novel datasets (often in partnership with commercial or government entities, ranging from financial transactions in Italy, to phone data in China, to health insurance records for the whole USA, to twitter data from the whole world), to study naturally occurring online and offline networks.