and Computational Social Science: Possibilities and Perils By

We live life in the network. We check our e-mails regularly, make mobile phone calls from almost any location … make purchases with credit cards … [and] maintain friendships through online social networks. … These transactions leave digital traces that can be compiled into compre-hensive pictures of both individual and group behavior, with the potential to transform our understanding of our lives, organizations, and societies. —Lazer et al. (2009, 721). Powerful computational resources combined with the availability of massive social media data-sets has given rise to a growing body of work that uses a combination of machine learning, natural language processing, network analysis, and statis-tics for the measurement of population structure and human behavior at unprecedented scale. however, mounting evidence suggests that many of the forecasts and analyses being produced misrepresent the real world. —Ruths and Pfeffer (2014, 1063) The exponential growth in “the volume, velocity and variability ” (Dumbill 2012, 2) of structured and unstructured social data has confronted fields such as political science, soci-ology, psychology, information systems, public health, public policy, and communication with a unique challenge: how can scientists best use computational tools to analyze such data, prob-lematical as they may be, with the goal of understanding individuals and their interac-tions within social systems? The unprecedented availability of information on discrete behav