Probabilistic topic modelling is a machine learning technique that has recently begun to find application in the social sciences. With almost no human supervision, probabilistic topic models can infer the thematic structure of large textual datasets, making them an appealing tool for scholars in fields such as communication studies, where such datasets are increasingly common. However, topic models also present social scientists with a range of conceptual and practical challenges, many of which are yet to be satisfactorily resolved. Far from making life simpler for social scientists, the outputs of topic models can be bewildering, not only because of their complexity-a model may include dozens of topics, each of which is defined by dozens of terms-but also because of their multiplicity, since a topic model can produce not one, but infinitely many, subtly different sets of topics to describe a given dataset. Further difficulties arise from the complexity of the data itself: in social science, textual datasets often represent diverse assemblages of actors, and the meaning of the texts may depend on the circumstances of their production as much as on their textual content.xiii