Methodological Contributions of Computational Social Science to Sociology

Currently, the data environment for sociology is changing dramatically (Salganik, 2018). Traditionally, the main type of data used by quantitative sociology was survey data. Collecting survey data entailed significant financial and human costs; therefore, the data provided by surveys were scarce. Such data are clean, structured, and collected by probability sampling. In the digital age, however, people’s behavior is observed daily and recorded constantly, which creates vast amounts of behavioral data known as digital traces (Golder & Macy, 2014). In addition, surveys and experiments using crowdworkers, based on nonprobabilistic samples, are by far the least expensive and can be collected in large quantities, and they are suitable for various interventions (Salganik, 2018). In this digital age of computational social science, data are messy and unstructured yet abundant.