Computational Social Science: Theories, Methods and Data

Due to the increasing availability of large-scale data on human behavior collected on the social web, as well as advances in analyzing larger and larger data sets, interest in applying computer science methods to address research questions in the social sciences continues to grow. “Big Data” researchers and “Data Scientists” entering the interdisciplinary field of Computational Social Science (CSS) often lack background in theories and methods in sociology, whereas sociologists are often not aware of data collection and analysis techniques in computer science. This tutorial helps to bridge this gap by providing an introduction (i) to social theories and models that help to understand the process that generated the data, as well as (ii) to statistical and computational methods that are useful for addressing social science research questions with observational data as found on the Web. The goal of this tutorial is to give participants a rich repertoire of methods that help to answer not only interesting “how” questions but also more fundamental “why” questions. This includes both a general knowledge of important sociological theories and how to derive verifiable hypotheses from them, as well as a good grasp of methods for causal inference from observational data to go beyond mere correlations. To maximize the learning outcome, there will be a set of short practical examples provided in the form of IPython notebooks. Furthermore, there will be a small competition where students are invited to submit their own research proposals. This course is an extended version of a tutorial given at WWW’16 together with Markus Strohmaier, Claudia Wagner, and Luca Aiello.