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With an increasing amount of data on every aspect of our daily activities – from what we buy, to where we travel, to who we know, and beyond – we are able to measure human behavior with precision largely thought impossible just a decade ago, creating an unprecedented opportunity to address longstanding questions in the social sciences. Leveraging this sea of information, however, requires both scalable computational tools, and understanding how the substantive scientific questions should drive the data analysis. Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science fills this role, using large-scale demographic, behavioral and network data to investigate human activity and relationships.

To give one illustratative example of this computational approach to social science, consider the question of how new ideas, behaviors and products gain popularity, an enduring topic in sociology and economics that has attracted significant interest among the scientific community and the general pubic alike. Over the last several decades, a theory of adoption has developed that proposes ideas diffuse through connected populations in the manner of a biological contagion, starting from a few initial adopters who “infect” their contacts, who then go on to infect their contacts, and so on until a large number of individuals have adopted. Historically, it has been prohibitively difficult to directly observe the adoption process in action, and accordingly to determine the extent to which prevailing theoretical models reflect reality. The last few years, however, have brought about a dramatic shift: with the ability to now observe billions of products and ideas propagating through social networking sites – a computational task facilitated by modern distributed computing frameworks – we can describe and characterize the diffusion process with groundbreaking rigor and detail.

Computational social science is a fundamentally interdisciplinary subject, drawing on expertise in distributed algorithms, scalable statistical and machine learning methods, and several substantive social science fields, including sociology, economics, psychology, political science, and marketing. Given the area’s nascence and the wide range of necessary skills, we work collaboratively and value a broad range of perspectives.