Fifteen years ago, as an undergraduate computer science student in the United Kingdom, I read a popular science article (Matthews, 1999) profiling the research of my now colleague, Duncan Watts. This article, about the science of small-world networks, changed my life. To understand why, however, it is necessary to know that in the United Kingdom, there is (or at least was during the 1980s and 1990s) a profound “them-versus-us” split between the STEM (science, technology, engineering, and mathematics) fields and all other disciplines. This split is amplified or perhaps even caused by the fact that people specialize at a very young age, choosing at 15 or 16 whether they will ever take another math course or write another essay again. I, like everyone else in my degree program, had chosen STEM, but my decision had not been easy – I had also wanted to study the social sciences. The article about Duncan's research changed my life because it had never before occurred to me that math and computers could be used to study social phenomena. For the first time, I realized that, rather than studying either computer science or the social sciences, perhaps I could study both. This, then, became my motivating goal.

Ten years ago, as a PhD student studying machine learning, I was not really any closer to my goal. Sure, there was a growing number of researchers studying social networks, but for the most part these researchers were physicists, mathematicians, computer scientists, social scientists, with little interaction between the groups. In contrast, in 2015, we are on the cusp of a new era. Over the past five years, the nascent field of computational social science has taken off, with universities and corporations alike creating interdisciplinary computational social science research institutes. This investment has, in part, been fueled an explosion of interest in “big data.” Whereas this term used to refer to the massive data sets typically found in physics or biology, the data sets that fall under this new big data umbrella are, for the most part, granular, social data sets – that is, they document the attributes, actions, and interactions of individual people going about their everyday lives (Wallach, 2014). Consequently, research on aggregating and analyzing social data ismore important (and better funded) than ever before.